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Hooked on hope: Former opioid user, new mom has 'so much to live for' – The Telegram

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Sydney Gordon could be dead. A statistic. A victim of the drug culture.

“I always had the potential for addiction. I was an angry and an unhappy teenager. In high school I was sexually abused by boys I knew and I hated it. I hated myself. Drugs were available and they let me escape. I could get away from myself and the trauma. I could sleep.”

The hurt continued into adulthood.

“The men I dated abused me. I’ve been sexually assaulted and physically beaten. I’ve been raped more than once. I just accepted that as the way my life would be.”

“I am an addict. I’ll always be an addict.”

  At 17 Sydney Gordon’s life spun out of control when she became addicted to opioids. At 29, she’s been free of illicit opioids for three years. Her one-year-old son, Harrison, is the centre of her life.
At 17 Sydney Gordon’s life spun out of control when she became addicted to opioids. At 29, she’s been free of illicit opioids for three years. Her one-year-old son, Harrison, is the centre of her life.

Gordon, 29, is a single mother on a methadone regimen and, in spite of being haunted by the nightmares of her past, she is hopeful about her future. She watches clouds drift over the ocean from her small Dublin Shore home as she nurses Harrison, her son, who just turned one.

“I am an addict. I’ll always be an addict, but I’ve been clean for almost three years and I never want to go back to the dark places, the horrors I’ve lived,” she said looking down at her son.

Gordon started using cannabis and drinking on a regular basis when she was thirteen. At seventeen she became addicted to opioids when her supplier suggested she “try something different.”

“I figured those pills were prescribed by a doctor so what harm could they cause? I didn’t know I was getting into something so heavy.”

Gordon was addicted to opioids “within a few weeks” and for seven years she crushed pills and snorted the powder.

“I knew I was in trouble. I hated the addiction. I went through detox a number of times, but I only went when I ran out of money for drugs. I hated myself. I hated the drugs, but I always went back to my supplier when I could pay for them.”

Drugs continued to tighten their grip on her life.

“After seven years of snorting I started to inject opioids. It got to the point where I was injecting myself 20 times a day. I could lose myself in the euphoria intravenous use gave me, but at the same time I detested my failure to get away from drugs and face my problems without being high.”

A close up view of a syringe with hypodermic needle and a droplet of fluid. - 123RF Stock Photo
A close up view of a syringe with hypodermic needle and a droplet of fluid. – 123RF Stock Photo

In 2010, in a desperate attempt to escape her self-hatred and the drugs, Gordon impulsively bought a ticket to Europe where she worked as a nanny in Spain. She hoped being away would free her from her addiction, but her demons followed her.

“I drank two bottles of wine nearly every day and, when I could get it, I used cocaine.”

After 10 months in Europe she returned to Nova Scotia. She told herself that she had left her addiction to opioids behind, but in her heart she knew she was lying to herself. As her plane approached Halifax she found the effects of withdrawal and the anticipation of getting “a fix” surfaced with a vengeance.

“Suddenly I was an emotional and physical wreck. I was out of control. It was a nightmare. My mind was dominated by past trauma, I was hot. I was cold. I was sweaty. I was shaking. Nauseous. I was very, very depressed.”

She was suffering the symptoms of withdrawal. Gordon called her supplier before her plane touched down.

For the next several years Gordon’s life was a terrible mix of drug induced euphoria, self-hatred, and futile attempts to free herself from both.

At one point, when she was in rehab, Gordon reconnected with an old friend, a crystal meth user. They agreed to travel together to Edmonton to get away from the drug scene in Nova Scotia. They thought that because they were addicted to different drugs they could help each other get free of their addictions in a new environment.

Instead, things went downhill fast. Gordon and her new boyfriend were quickly drawn into the drug culture in Edmonton. Both became regular users of a concoction of heavy drugs including crystal meth, cocaine, and fentanyl. Gordon shared her IV needles and opioids with the man who had promised to help her get off drugs.

“I couldn’t watch them die. They were near death when I injected them with Naloxone and thankfully they came around.”

Paying for her drugs was always a problem for Gordon. She worked when she could, but the money she earned wasn’t enough.

“In Edmonton we lived in flop houses with other drug addicts. Everyone was so desperate to get high and stay high. We weren’t friends. We just used each other to get drugs. We stole from each other. We stole from anyone we could. We were reckless. I never sold sex, but I lived with men and women who did.”

Gordon saved the lives of two people who were the victims of a “hot shot.”

“A hot shot is when one addict intentionally gives another a lethal overdose in order to steal their money or drugs. I saw that happen twice. I couldn’t watch them die. They were near death when I injected them with Naloxone and thankfully they came around.”

ROCK BOTTOM: HOMELESS, SICK AND 80 POUNDS

After 10 months Gordon found herself homeless in an Edmonton winter. She had contracted Hepatitis C from dirty needles. She weighed 80 pounds.

Her life had hit rock bottom.

“I had to leave my boyfriend and get out. I knew we would both die if we stayed there. Freezing to death, being murdered. Suicide was something I thought about.”

Gordon came home to Dublin Shore feeling that she had one last chance to turn her life around.

She and her mother approached Dr. David Martell who arranged for Gordon to receive her initial treatment at the Opioid Replacement Treatment Program in the Annapolis Valley. Eventually The South Shore Opioid Outreach Team, based in Lunenburg and Queens counties, had space for her which meant that she could continue methadone therapy closer to her home under Dr. Martell’s supervision.

“He’s a great and wonderful doctor. I can’t say how much he’s helping me.”

“I’m happy. I’m finally happy.” 

At the same time she sought medical help, Gordon disassociated herself from former acquaintances who she knew were still part of the drug scene. And she distanced herself from the people who had taken advantage of her sexual vulnerability.

“I know now that I was always running away, from myself, from my addiction, and from the people who could see what was happening and tried to help. I was terribly hurt and I hurt everyone who cared about me.”

Since she’s been home, Gordon been treated successfully for Hepatitis C. She has renewed friendships, building a network of people who give her love and support. She has a part-time job. Dr. Martell is gradually reducing her methadone dosage. She has now been free from illicit opioids for three years.

She has turned her life around.

“I’m happy. I’m finally happy,” she said. She looked down at her son and hugged him. “I have so much to live for.”

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Naloxone – “Who is your kit for?” from Nova Scotia Health Authority on Vimeo.


Opioid outreach: Healing the person, not just the addiction

The South Shore Opioid Outreach Team works with patients living with substance use disorder. From the left, Amanda Noble, LPN, Dr. Dave Martell, Gill Landry, social worker, Tara Grant, LPN, Dr. Pascal Gellrich, and administrator Krisanne Tanner-McLain. The Team offers counselling and therapy to help patients deal with addiction and with the trauma that underlies addiction. Dr. Elizabeth King could not be present for the photograph. - PETER BARSS PHOTO
The South Shore Opioid Outreach Team works with patients living with substance use disorder. From the left, Amanda Noble, LPN, Dr. Dave Martell, Gill Landry, social worker, Tara Grant, LPN, Dr. Pascal Gellrich, and administrator Krisanne Tanner-McLain. The Team offers counselling and therapy to help patients deal with addiction and with the trauma that underlies addiction. Dr. Elizabeth King could not be present for the photograph. – PETER BARSS PHOTO

Dr. Dave Martell gave up a successful 20 year family practice to devote all of his time to helping those suffering from opioid use disorder.

“It took me two years to make the decision. I had to say goodbye to 1,500 people who had been my patients for many years without knowing if they would be able to access care after I left. It was a very, very difficult choice to make.”

Dr. Martell said he wanted have the “biggest impact” he could as a doctor. Along with Dr. Pascal Gellrich, he formed the South Shore Opioid Outreach Team in March 2018. The Outreach Team includes three physicians, two Licensed Practical Nurses, a social worker and an administrator. Case management is a shared role. It is the first opioid use disorder clinic on the South Shore formally funded by the Nova Scotia Health Authority.

Headlines about illicit opioid use are alarming. According to the Government of Canada website there were 11,000 opioid-related deaths in Canada between January 2016 and December 2018. In Nova Scotia there have been an average of 60 deaths every year between 2013 and 2018.

While images of first responders rushing to save someone near death in a Vancouver back alley are dramatic, they encourage us to see the “opioid crisis” in oversimplified terms. The problem is more extensive and more complicated than reviving someone who has overdosed.

The South Shore Opioid Outreach Treatment Team provides medication therapy for illicit opioid use and help with other physical health needs including vaccinations, acute care, and Hepatitis C treatment. But Dr. Martell says there’s more to be done than just filling medical needs.

“Illicit drug use is a symptom of complex problems. The whole person must be treated,” he said.

“A major component of our team’s approach is to help patients establish a normal routine in their lives by addressing a wide spectrum of issues including simplifying getting to appointments on time, working out problems associated with income and housing, and supporting those who have suffered childhood trauma.”


WHERE TO GET HELP

The South Shore Opioid Outreach Team conducts clinics once a week in each of the following locations:

  • Bridgewater, Thursdays 9 a.m. to noon
  • Caledonia, Mondays 9 a.m. to noon
  • New Germany, Wednesday, 4 p.m. to 8 pm.
  • For further information or to make an appointment call 902-634-8807 extension 1713307.

For more addiction treatment services in your province, follow these links: 


There are three clinics along Nova Scotia’s South Shore: Bridgewater, New Germany, and Caledonia. In addition, services are often provided at the central hub in Lunenburg at the Wellness Clinic.

“Travelling to appointments is a major barrier to treatment. We are helping with that problem by establishing clinics in small rural towns.”

The first step toward treatment is an hour long discussion with members of the outreach team to assess the patient’s specific needs.

“There is no waiting list,” Dr. Martell said. “Assessments can be done anytime during normal working hours, Monday through Friday between 8:30 and 4:00. Treatments generally start the next available clinic day.”

The central purpose of the assessment is to discover deep-seated problems that are at the root of the opioid use. In most cases the euphoria provided by drug abuse is an escape from the stress of painful realities including psychiatric problems, poverty, health issues like HIV and Hepatitis C, physical trauma, childhood neglect, and the misuse of other drugs, including alcohol.

Detoxification is an attempt to alleviate withdrawal symptoms, usually with a longer acting drug that can be administered in a controlled way to comfortably lower the dose while the body adjusts. This approach, by itself, has a dismal success rate because it fails to deal with the primary causes that are intertwined with addiction.

The outreach team views illicit opioid use as a symptom of a collection of personal problems the patient faces. After the initial assessment, the Team devises a treatment strategy that begins by addressing the drug use and, just as importantly, the deep-seated pain that has accumulated over the patient’s lifetime.

Patients are immediately offered medication to eliminate withdrawal symptoms. The medication, a less potent opioid, is administered by a pharmacy to diminish craving and to minimize the physical distress of withdrawal. The patient is monitored by the pharmacy to ensure adherence to the program.

“Our first priority is to stabilize the substance use disorder. However, when a patient presents significant mental health or acute physical health problems we treat both conditions at the same time.”

Over time, the dosage of the replacement drug is reduced, and, if there is no evidence of illicit drug use and if the patient is in a stable social environment, the patient earns the privilege of taking medication doses at home. Even then, “the patient must be tested periodically to determine that continued dosing of the medication is safe for the patient and for the community.”

In addition to administrating medication, the community pharmacist provides some psychological and social support.

During treatment, the outreach team offers support and counselling tailored to meet outcomes chosen by the patient.

“The goals of treatment are set by the patient and those goals are not always to completely abstain from drugs,” Martell said.

“Our objective is harm reduction. Substance use can be made safer without a focus on, or requirement for, abstinence. Opioid use disorder is a chronic disease. There is no endpoint to treatment. Success is measured by improved psychological and social functioning – staying out of jail, reconciling with family, maintaining employment, furthering education. Sometimes success is having less risk of contracting HIV or Hepatitis C, or being subjected to less violence.”

In short, the program offers hope for opioid users, not for a perfect life, but for a better, safer life.


Opioid addiction: Arrest can be first step to recovery

In 2013 RCMP Corporal Ted Munro was part of a committee that was established to understand the complexities of opioid addiction and to break down the stigmatism towards opioid users through education. He says his role as a police officer and as member of his community includes helping opioid users gain access to treatment. - PETER BARSS PHOTO
In 2013 RCMP Corporal Ted Munro was part of a committee that was established to understand the complexities of opioid addiction and to break down the stigmatism towards opioid users through education. He says his role as a police officer and as member of his community includes helping opioid users gain access to treatment. – PETER BARSS PHOTO

Opioid addiction can be so devastating that some seek to end their lives rather than endure them.

And for many, the first step toward treatment comes when a police officer knocks on their door.

Arrest for illegal possession is feared. Not many illicit opioid users would expect the police to extend a hand to help them deal with their drug problem.

But sometimes compassion comes in a uniform.

RCMP Corporal Ted Munro of the Bridgewater detachment has seen the worst of opioid addiction and known those who have died.

He recalled a grown man lying on the floor in a fetal position at his detachment in Cookville. The man was crying and begging Munro to unlock the exhibit room and give him drugs held for a trial so that he could quiet the pain of withdrawal. He knows mothers so driven by their addiction that they have left their babies alone to go and buy opioids. Young girls have asked him if they can get treatment sooner if they get pregnant.

While he’s describing a young farmer who suffers from opioid addiction he seems to forget that he’s being interviewed. He stares out the window. He gets choked up. There are tears in his eyes.

Cpl. Munro talks about opioid users who have been rejected by their friends and their parents, socially isolated because those close to them can’t cope with their behaviour.

Fentanyl pills.
Fentanyl pills.

Illicit opioid users are so compelled to obtain drugs that they commit crimes that almost guarantee getting caught—shoplifting, robbing the homes of neighbours, writing bad cheques, stealing copper wire and pipe, and robbing stores where the staff know them.

Do users want to free themselves from addiction?

“One hundred percent,” Munro says.

The Mountie has made a point of understanding the complexities of drug addiction. In 2013 he helped form the South Shore Opiate Committee which included police, probation officials, pharmacists, representatives from social services and child protection services, and income assistance personnel.

“We met monthly for a couple of years. The committee was formed to discover the extent of addiction in this area, to educate ourselves and everyone concerned about addiction, and to understand the social problems it creates. With that knowledge we developed best practices to deal with all aspects of addiction.”

While the decision to seek treatment has to be up to the substance user, Cpl. Munro does everything he can to help. If an opioid user chooses to seek help, Munro will make a referral to a clinic that offers drug substitution therapy and counselling or to a doctor who has the training to offer treatment. One of the first steps in treatment is taking methadone under carefully monitored conditions at a pharmacy. When RCMP officers escort patients to their appointments they do so “as discreetly as possible.”

“We know every back door of every pharmacy in the county.”

Treatment can last for months and even years. As long as he is in contact with an addict, Munro offers whatever emotional support he can.

“These are people who have lost family and friends. They have lost their identity. They are broken. I tell them that their families and friends will take them back. Once they’ve chosen treatment they can look forward to support from the larger community. I’m part of that community.”


Police, province working together on solutions to opioid crisis

Dr. Robert Strang, Nova Scotia’s Chief Medical Officer of Health, says that opioid addiction is on the increase in Nova Scotia. While the province’s Opioid Use and Overdose Framework is keeping pace with the increase, Dr. Strang says a “drug free” society is a long way off. - PETER BARSS PHOTO
Dr. Robert Strang, Nova Scotia’s Chief Medical Officer of Health, says that opioid addiction is on the increase in Nova Scotia. While the province’s Opioid Use and Overdose Framework is keeping pace with the increase, Dr. Strang says a “drug free” society is a long way off. – PETER BARSS PHOTO

No place is immune to the impacts of opioid use and misuse. Lives are ruined and people are dying in the cities and in every rural community.

“The illicit use of opioids is increasing right across Canada,” according to Dr. Robert Strang, Nova Scotia’s Chief Medical Officer of Health. Since 2011, he asid, an average of 60 Nova Scotians have died each year from overdosing on opioids.

 Strang says availability is increasing, but the number of overdoses and deaths due to overdoses has remained relatively stable.

“The Opioid Use and Overdose Framework is holding the line in spite of the fact that we are seeing more of these drugs.”

The program is helping, Strang said, but more needs to be done.

“We have a great deal of work ahead of us.”

READ: Nova Scotia’s Opioid Use and Overdose Framework Update 

GETTING THE DATA

The Department of Health and Wellness tracks opioid overdose deaths monthly, reports them to a national data base, and posts them online.  Emergency Health Services calls to where Naloxone is administered are now also being tracked.

In an effort to reduce the use of opioids, Nova Scotia joined the government of British Columbia and Health Canada to launch an anti-stigma marketing campaign in June 2018. The province is also training police to “help direct individuals facing addiction and trauma to proper care”.  

 GETTING HELP

A Naloxone kit.
A Naloxone kit.

“Those who misuse drugs tend to see the police as their enemy,” Dr. Strang said. “But, as long as they’re not selling drugs, we are more interested in seeing those people in treatment instead of being prosecuted and put in jail.”

 Education, in schools and in society at large, is another avenue the province is pursuing to stem the use of opioids. Dr. Strang emphasizes that understanding the social and emotional trauma many opioid users face is at the heart of making a truly significant difference in drug use.

 Realistically, opioid abuse will be with us for the foreseeable future. 

“As long as we have poverty, physical and sexual abuse…as long as we have these and other social ills we will have a drug problem,” Strang said.

HARM REDUCTION

To reduce the number of overdose deaths, the province has established a harm reduction team to oversee funding and to “work toward safer consumption models”. The province has provided $2.76 million ($1.38 million annually over the last two years) to fund three community-based harm reduction organizations in the province that provide needle exchange, distribute Naloxone kits and “support individuals to use drugs in a safer way.”  The province is also funding community pharmacies so they can provide free Naloxone kits and free training in their use to all Nova Scotians who request it.   

“The kits are free for the asking,” Dr. Strang said. “If there is any reason you might think you would witness someone overdosing we urge you to pick up a kit. Up to this point 10,000 kits have been given out and hundred and thirty-five opioid overdoses have been reversed with Naloxone injections. That’s a hundred and thirty-five lives saved.”

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TREATMENT AND PRESCRIBING PRACTICES

In partnership with the Federal Emergency Treatment Fund, Nova Scotia has increased funding to $1.8 million annually for “opioid use disorder treatment expansion”. There are five satellite treatment clinics available in Nova Scotia now including one in Bridgewater, one in Caledonia, and one in New Germany all operated by the South Shore Opioid Outreach Team.

“For those seeking treatment, wait time for an appointment has been a serious impediment,” Dr. Strang said. “Now, anyone who has made the choice to get help should be able to get it right away.”

The number of people waiting for opioid use disorder treatment has been reduced from about 250 before November 2017 to 25 people as of September 1, 2019.

CRIMINAL JUSTICE AND ENFORCEMENT

Workshops on opioids have been conducted for the police and front line-line investigators and first responders. Naloxone kits have been supplied to police, sheriffs, and, correctional facility personnel. With assistance from the province, The Nova Scotia Chiefs of Police established a drug committee to provide direction on the issues of drugs, including opioids. 

“Opioids not only damage individuals, they damage families, and they damage our society in general,” Dr. Strang said. “Our programs are constantly evolving, changing, and getting better. It’s a long way off, but I’m hopeful that someday we’ll live in a society free of illicit drug use.”


FURTHER READING: Click here for an overview of Canada’s opioid crisis.


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The challenges integrating U=U into HIV care around the world – aidsmap

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Advocates from around the world came together at the U=U Global Summit at the 24th International AIDS Conference (AIDS 2022) in Montreal last month to share successes and challenges that continue to hamper full-scale integration of the ‘Undetectable = Untransmittable’ (U=U) message in diverse global contexts.

A central theme was that structural barriers – especially poverty, limited access to treatment and viral load testing, stigma, and widespread inequalities – continue to shape health outcomes. HIV criminalisation is also a formidable barrier in many contexts, and advocates discussed the possible role of U=U in challenging HIV criminal laws.

The Caribbean

Judy-Ann Nugent, from the Jamaican Network of Seropositives (JN+), spoke about challenges in the Caribbean, where there has been limited U=U buy-in from healthcare providers and people living with HIV. She emphasised the role of stigma, poverty, weak health systems and low levels of literacy in limiting treatment uptake and adherence.

“Simply put, if people are not fed, paid – have enough money or food – if their basic needs are not met, taking HIV medication will not be a priority for them,” she said.

[embedded content]
Led by activist Michael Ighadoro the entire AIDS 2022 conference stands up for U=U.

However, there has been progress, with 70% of all people living with HIV in the region accessing treatment in 2021 and incidence continuing to drop. According to the latest UNAIDS data, 84% of people living with HIV in the Caribbean know their status, 83% are on treatment and 87% are virally suppressed.

To promote more widespread awareness of U=U, Nugent recommended that U=U messaging is embedded in funding agreements with PEPFAR and the Global Fund so that countries are required to take proactive steps to integrate U=U into national programmes in order to receive funding. PEPFAR’s updated country guidance for 2022 does just this, making extensive mention of the need for countries to integrate U=U messaging along the HIV care continuum.

Latin America

Dr Franco Bova, from the Argentinian organisation Asociación Ciclo Positivo, shared that only 60% of those on treatment are virally suppressed in Latin America, falling far short of the previous 90 and the current 95 targets for viral suppression. It is also one of the regions where HIV incidence has increased since 2020. Bova said poverty and inequality perpetuate new infections and are barriers preventing people living with HIV from achieving viral suppression.

Various approaches have been successful at creating awareness of U=U in the region. In Argentina, activists have worked with community-based organisations, NGOs, universities, and local government to spread the U=U message at large public events, such as Pride, and through social media. Bova spoke about some successful strategies used in other Latin American countries, such as storytelling in Mexico, music videos and concerts in Venezuela and official government campaigns in Brazil. He also highlighted important gaps that make it challenging to speak about U=U at all. For instance, in Peru, the Ministry of Health does not collect any data on viral suppression.

Bova’s organisation is promoting a virtual platform, Indetectable LAC, to bring stakeholders in Latin America and the Caribbean together to share information and to enable better networking in the regions.

The Middle East and North Africa

HIV infections increased by 33% in this region from 2020 to 2021. It is one of only three global regions, along with Latin America, and eastern Europe and central Asia, where HIV is still on the rise. In 2021, only 67% of people living with HIV knew their status, 50% were on treatment and 44% were virally suppressed.

“The Middle East and North Africa is the region where the international HIV community has failed,” stated Arda Karapinar, founder of Red Ribbon Istanbul, Turkey’s leading HIV civil society organisation. He emphasised the distinct contextual challenges in the region. HIV-related stigma, combined with conservative religious attitudes towards sex and limited human rights, present formidable challenges in getting the U=U message out.

However, he also spoke of how passionate local activism can result in change and create awareness. “I know from my own experience in Turkey how sometimes, just one activist from a country or a region, dedicated to creating a change in society for the benefit of all, may be highly sufficient. There are great activists in the region who are defending U=U. They continue to work despite countless risks.”

Karapinar argued that Turkey is uniquely positioned between Europe and the Middle East, and can act as a meeting point and a safe harbour for those hoping to improve HIV outcomes and U=U awareness in the Middle East and North Africa region.

United Kingdom

Activist Fungai Murau spoke about the gaps that still exist in U=U awareness, even in the UK. She shared the story of a young woman who had acquired HIV vertically and had never heard about U=U. “Children who acquired HIV vertically in the UK are being transferred from adolescent clinics to adult clinics without being told about U=U,” she said. “Because we are assuming that paediatric doctors should not be talking to young girls about sex. This is not correct. We need to change that. We need to ensure that by the time they transfer to adult clinics, we have closed that gap.”

She advocated for integration across different healthcare services in the UK. “My HIV clinic is my champion, but my GP or my dentist may not know about U=U.”

Criminalisation in the United States

The US is one of the leading countries criminalising people with HIV under laws ranging from non-disclosure to alleged transmission. Convictions under these laws can result in lengthy prison terms and registration as a sex offender.

[embedded content]
Krishen Samuel, Florence Anam and Professor Linda-Gail Bekker discuss U=U in our aidsmapLIVE AIDS 2022 special.

While some activists have argued that U=U should be used as a basis for decriminalisation, Catherine Hanssens, founder of the Center for HIV Law and Policy, spoke about the potential pitfalls of being overly reliant on U=U when advocating for HIV decriminalisation, particularly because of the structural barriers to achieving viral suppression in the US.

Hanssens emphasised that advocacy on behalf of an individual is very different from advocacy for equitable policy and law reform. While it may certainly be beneficial to show proof of undetectability (and subsequent lack of ability to transmit HIV) in individual cases, there might be unintended negative consequences if advocates call for undetectable status to be codified into laws – especially for the groups most likely to be targeted by HIV criminalisation.

Glossary

Undetectable = Untransmittable (U=U)

U=U stands for Undetectable = Untransmittable. It means that when a person living with HIV is on regular treatment that lowers the amount of virus in their body to undetectable levels, there is zero risk of passing on HIV to their partners. The low level of virus is described as an undetectable viral load. 

virological suppression

Halting of the function or replication of a virus. In HIV, optimal viral suppression is measured as the reduction of viral load (HIV RNA) to undetectable levels and is the goal of antiretroviral therapy.

stigma

Social attitudes that suggest that having a particular illness or being in a particular situation is something to be ashamed of. Stigma can be questioned and challenged.

criminalisation

In HIV, usually refers to legal jurisdictions which prosecute people living with HIV who have – or are believed to have – put others at risk of acquiring HIV (exposure to HIV). Other jurisdictions criminalise people who do not disclose their HIV status to sexual partners as well as actual cases of HIV transmission. 

If viral load is a factor in determining whether a person is guilty, it can lead to using a person’s failure to stay in health care or to achieve viral suppression as evidence of guilt. It can also lead policymakers and prosecutors to believe, and argue, that people living with HIV who are not undetectable pose a significant risk of transmission to sexual partners. “Current science makes it clear that HIV is not easy to transmit,” Hanssens said. “And even when transmitted, it is easily survivable with appropriate treatment.”

She argued that efforts to reform HIV criminal laws should be based on whether intent to harm was present or not, and the fact that HIV is a manageable chronic illness with appropriate treatment, not a death sentence.

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newsGP – COVID has had 'profound' mental health impact on mothers – RACGP

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News


Many women experiencing mental health issues during lockdowns did not access support from GPs or psychologists, new study findings show.

Less than half of a cohort of surveyed women received support from a GP or psychologist for mental health difficulties during the pandemic.

It is no revelation that the mental health and wellbeing of many people has been impacted by the COVID-19 pandemic.
 
However, new findings indicate that for women, particularly mothers, that impact has been ‘profound’.
 
‘The pandemic has highlighted gaps in the current service delivery frameworks, especially for women with limited financial resources,’ social epidemiologist Professor Stephanie Brown said.
 
‘These gaps have resulted in many women in need of mental health support being unable to access mental health services.’
 
Professor Brown is Head of Intergenerational Health at the Murdoch Children’s Research Institute (MCRI) and led the Mother’s and Young People’s Study used to inform a policy brief on the pandemic’s impact on maternal mental health and wellbeing.
 
The prospective cohort study was originally investigating women’s health after childbirth, but expanded to include children and young people’s health and wellbeing, and how it links with their mother’s.
 
It identified that gaps in current health service delivery had widened during the pandemic, resulting in many women being unable to access appropriate services for mental health support.
 
In response, the researchers are calling for further policy action, including an extension of mental health strategies across the whole family.
 
‘It is important to provide multi-service frameworks that enable mothers, fathers, children and young people under 18 to receive appropriately tailored support,’ Professor Brown said.
 
According to an online survey of 418 women conducted as part of the study during Victoria’s second lockdown, almost one in three women reported ‘clinically significant’ mental health issues.
 
Notably, less than half (45%) of these women received support from health professionals, with just one in four talking to a GP or a psychologist.
 
More than half (55%) did not receive any mental health support from primary care or mental health services, and only 4% of women experiencing ‘clinically significant’ depression or anxiety had called a telephone support line.
 
The reported reasons for not receiving support from health professionals included prioritising support for their children’s mental health over their own, psychologists closing their books to new clients/long waiting periods, and a lack of confidence using telehealth.
 
Additionally, women experiencing mental health issues were almost four times more likely to delay their own medical care due to the cost of services.
 
Chair of RACGP Specific Interests Psychological Medicine Dr Cathy Andronis is not surprised by the findings.
 
‘The sense of isolation and disconnection from normal life as a result of the pandemic leaves many vulnerable people feeling abandoned by others and exacerbates underlying negative emotions 
and thoughts, leading frequently to helplessness and hopelessness,’ Dr Andronis told newsGP.
 
‘People give up on asking for help, particularly when there are urgent tasks at hand such as caring for a new baby that is needy night and day, and more helpless than their mother.’
 
The RACGP has recently raised a number of concerns around current Medicare structures for providing mental health care, leading to fragmentation and poor patient outcomes.
 
Acknowledging GPs’ essential place in providing mental health care, the college is lobbying for this space to be properly funded, including by implementing higher Medicare rebates for longer
consultations.
 
And although telehealth has helped expand access to care, Dr Andronis believes it is not always appropriate when it comes to mental health care.
 
‘The lack of human physical connection of telehealth services exacerbates this isolation [experienced by women in the study],’ she said.
 
‘We need real human, face-to-face connection when we are most distressed. Empathy and compassion online or over the phone is usually not as effective or responsive as live consultations.  
 
‘Fear by mothers of bringing COVID into their household was one more major stress that needed to be avoided when they were just coping with the necessary adjustments of the postnatal period.’
 
The Mother’s and Young People’s Study survey cohort also revealed that many women experienced:
 

  • fatigue (53%)
  • anxiety (41%)
  • irritability (33%)
  • sadness (27%)
  • loneliness (21).

 
In January and April 2021, when many restrictions were lifted, 391 women took part in a subsequent survey which revealed that despite reports of these issues being reduced, they remained ‘well above’ pre-pandemic levels.
 
Professor Brown said the findings are expected given the many family disruptions caused by the pandemic.
 
‘Much of the responsibility for remote schooling was shouldered by women,’ she said.
 
‘For some women, this meant giving up their paid job, taking leave without pay or reducing their hours of work significantly.
 
‘The challenges of remote learning were particularly acute for mothers of children experiencing neurodevelopmental conditions such as ADHD or autism, and for women whose children started at a new school just prior to the pandemic.’
 
While underlying mental health issues were exacerbated by the pandemic, one in five women with no prior history of depression also reported ‘clinically significant’ depressive symptoms during the pandemic.
 
One third of women from the study continue to experience significant mental health problems including ongoing fatigue and parenting stress.
 
Professor Brown says these ongoing impacts present further cause for policy action.
 
‘[The] continuing day-to-day effects of the pandemic are likely to have both short- and longer-term impacts on women’s workforce participation, their own mental health and wellbeing, and the mental health and wellbeing of other family members,’ she said.
 
The MCRI policy brief states that the ‘process of healing and recovery from the pandemic will take time’, suggesting GPs will continue to play a major role supporting mothers well beyond the perinatal period for years to come.
 
‘GPs have been the most accessible and available healthcare providers during this pandemic and are likely to continue to be so,’ Dr Andronis said.
 
‘We are able to meet these women in our clinics and offer timely support. We are highly appreciated by vulnerable people when we offer our support and hold hope for them.
 
‘Managing these life events and transitions, collaboratively with patients is something we do well.’
 
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Brain aging differs with cognitive ability regardless of education | Scientific Reports – Nature.com

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Abstract

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20–88 years, followed-up for up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.

Introduction

Does higher intelligence protect against brain atrophy in aging? Numerous findings motivate this question: General cognitive ability (GCA) is positively associated with brain volume and cortical characteristics at various life stages, including young adulthood and older age1,2,3,4,5. GCA is consistently associated with all-cause mortality and health, with higher GCA related to lower risk of diseases and lifestyle factors known to negatively affect brain health4. In part, associations are still found after controlling for factors such as educational attainment, suggesting that contemporary GCA in itself is of importance4. While higher education has been posited as a protective factor against neurodegenerative changes6,7, we recently documented in a large-scale study of multiple cohorts that education is not associated with rates of brain atrophy in aging8. A more promising candidate influence on brain aging may thus be GCA independently of education. Whether GCA level is predictive of longitudinal cortical change has primarily been investigated in older cohorts, and with mixed results9,10,11. The relationship of GCA level and cortical changes through the adult lifespan has to our knowledge hitherto not been investigated.

In this context, the lifespan perspective is critical and has implications for understanding functional loss in older age. Several studies indicate that people with higher GCA in young adulthood may be at lower risk of being diagnosed with neurodegenerative disorders in older age4,12,13. Recent findings from large datasets point to a relationship between family history of Alzheimer´s Disease (AD) and cognitive performance level four decades before the typical age of onset of AD14. However, GCA-AD risk associations have not been consistently observed, and mechanistic factors are poorly understood15. Possible explanations include both a brain reserve, i.e. “threshold model”16, as well as a brain maintenance17 account. The brain reserve model would entail that higher GCA as a trait is related to greater neuroanatomical volumes early in life, young adulthood inclusive, thus delaying the time when people fall below a functional threshold of neural resources in the face of neurodegenerative changes with age. This would happen even if such changes in absolute terms are of similar magnitude across different ability levels, i.e. slopes are parallel, indicating “preserved differentiation”18, where initial differences in young are upheld with age16,19. The brain maintenance17, or “differential preservation”18 account would on the other hand predict less brain change in adulthood for people of higher ability, and therefore a smaller risk of cognitive decline and dementia19. The brain reserve and maintenance accounts of the relationships between GCA, brain characteristics and clinical risk are not mutually exclusive, but their relative impact through the adult lifespan is unknown. Collectively, the current findings indicate a need to understand whether there is a relationship between GCA as a trait and brain changes, independently of education, over the adult lifespan.

We tested whether GCA predicted brain aging as indexed by cortical volume, area and thickness change measured longitudinally in 7002 MRI scans from several European cohorts covering the adult lifespan in the Lifebrain consortium20 and the UK Biobank (UKB)21,22 (n = 3327, age range 20–88 years at baseline, maximum scan interval of 11 years, see Online Methods for details). To disentangle possible environmental and genetic influences on the relationship between GCA and brain aging, we controlled for educational attainment in the main analyses, and in a second step for polygenic scores (PGSs) for education and GCA23,24. Established PGSs are only moderately predictive of GCA23, but in view of evidence that the polygenic signal clusters in genes involved in nervous system development23, we did expect such scores to explain part of the intercept effect, with no or weaker effects on brain aging. We expected any effects of GCA on cortical changes to apply to all ages, but in view of recent findings of greater relationships between brain and cognitive function in older than younger individuals3, we also tested the age interaction. Based on previous findings, including from broader cross-sectional Lifebrain cohorts25, and mixed results from smaller longitudinal older cohorts9,10,11, we hypothesized that GCA would be positively related to anatomically widely distributed cortical characteristics through the adult lifespan (intercept effect), but that associations with differences in cortical aging trajectories (slope effects) may be observed to a lesser extent. We expected effects of GCA to be at least partially independent of education8, both for intercept and slope associations.

Results

The main models of associations of GCA with cortical characteristics, and their change, were run separately for samples within the Lifebrain consortium (n = 1129, 2606 scans)20 and the UK Biobank (UKB, n = 2198, 4396 scans)21,22, and then meta-analyses were run on the results, using the metafor package26. Using the estimate and standard error at each vertex, random effects meta-analyses were conducted at each vertex separately. In all main models, sex, baseline age, scanner, time (interval from baseline) and education were entered as covariates. In modeling the effects of GCA on cortical characteristics (level-level analyses), GCA was entered as the predictor (explanatory variable), whereas in modeling the effects of GCA on brain aging (level-change analyses), the interaction term of GCA × time was entered as the predictor, and education × time was entered as an additional covariate along with GCA and education. Since brain aging (i.e. change) was of chief interest, we did not include intracranial volume (ICV), which is stable, in the main analyses. For direct comparison, we also then present level-level analyses without controlling for ICV. This was also chosen given the paucity of evidence for region-specific associations, and previous studies indicating that neuroanatomical volume in and of itself, when controlling for sex, may be associated with GCA9,25. Results from models including ICV, as well as models without education, as covariates, can be found in the Supplementary Information (SI). Additional analyses included the interaction term baseline age × time as a covariate, and in one set of analyses we entered the interaction term baseline age × time × GCA as predictor (with relevant two-way interaction terms as covariates), to test if effects differ reliably across the lifespan.

GCA level: brain level analyses

Cluster p-value maps across Lifebrain and UKB for the relationship of GCA and cortical characteristics controlled for education, are shown in Fig. 1. For cortical volume and area, there were widespread positive associations of GCA bilaterally across the cortical mantle seen in all lobes. For area, significant effects were seen across 47.6% and 44.2% of the left and right hemisphere surface, respectively. For volume, similar numbers were 37.4% and 19.5% for left and right, respectively.

Figure 1

Level-level associations: meta-analytic cluster p-value maps of the associations of general cognitive ability (GCA) and cortical characteristics, controlled for education. Meta-analytic cluster p-value maps (Lifebrain and UKB) of the relationships between GCA at baseline and cortical characteristics are shown, when age at baseline, sex, time (since first scan) and education are controlled for (p < 0.05, corrected using a cluster-forming p-value threshold of p < 0.01). Relationships are shown, from left to right for each panel: right and left lateral view, right and left medial view.

For cortical thickness, only minor positive effects were seen, in proximity of the left central sulcus, covering only 1.1% of the surface.

Results of analyses per sample, controlling and not controlling for education are shown in Supplementary Figs. 1 and 2. Effects were largely similar, though slightly more restricted spatially, when controlling, than when not controlling for education. When adding ICV as a covariate, the intercept effects for cortical volume and area in the meta-analysis shown in Fig. 1 became non-significant, with only a very small effect on cortical thickness in the left hemisphere remaining (see Supplementary Fig. 3), pointing to these being broad effects grounded in greater neuroanatomical structures in general, rather than being region-specific.

To show effect sizes, we calculated the effect of 1 SD increase in GCA on cortical volume. Across Lifebrain and UKB, 1 SD higher GCA was associated with 1.0% larger cortical volume. Effect size maps for level-level analyses showing the regional variation in effect sizes for each sample separately are shown in Supplementary Fig. 4. Effect sizes were numerically smaller in Lifebrain (0.6%) than in UKB (1.3%). Restricting the analyses to regions where significant effects were seen, 1 SD increase in GCA was associated with 2.0% larger cortical volume in Lifebrain and 1.6% larger volume in UKB, but please note that these latter effect sizes are inflated by being within significant regions. Similar analyses for cortical area showed that 0.8% larger area was associated with 1 SD higher GCA across the cortex, with effects being 0.6% in Lifebrain and 0.9% in UKB. Restricting the analyses to regions where significant effects were seen, 1 SD increase in GCA was associated with 1.6% larger cortical volume in Lifebrain and 1.2% larger volume in UKB, with the same caveat as above. For thickness, effects were minute: 0.06% across studies (Lifebrain 0.04%; UKB 0.08%). Within significant clusters (UKB only), the effects of 1 SD higher GCA was 0.8%.

GCA level: brain change analyses

Having confirmed the expected positive relationships between GCA and cortical volume and area controlled for education in terms of an intercept effect, we investigated the question of slope effects. Associations of GCA level at baseline and change in cortical characteristics, controlled for education, are shown in Fig. 2. As expected, effects were more spatially limited than those seen for intercept models, with only restricted regions showing significant relationships: Higher baseline GCA was associated with less regional cortical volume reduction in the left middle cingulate gyrus, a medial area around the central sulcus and a part of the lingual gyrus. The most extensive effects were seen for thickness change, where higher baseline GCA was associated with less thinning in regions corresponding to the volume effects, in addition to parts of the right anterior and lateral temporal cortex and an area in the most medial part of the intersection between the central sulcus and the superior frontal cortex. No associations with area change were observed Taken together, this means that the observed positive associations of GCA with volume change primarily reflect less cortical thinning with higher GCA. (See Supplementary Fig. 5 for result for each subsample separately).

Figure 2
figure 2

Level-change associations: meta-analytic cluster p-value maps of the associations of general cognitive ability (GCA) with change in cortical characteristics, controlled for the effect of education. The association is shown for the interaction of GCA at baseline and time (interval since baseline scan), when age, sex, time, GCA, education, and the interaction of education and time, are controlled for (p < 0.05, corrected using a cluster-forming p-value threshold of p < 0.01). Significant regions are shown, from left to right for each panel: right and left lateral view, right and left medial view.

Associations of GCA with cortical change were essentially unaffected by adding ICV as a covariate (Supplementary Fig. 6).

In order to illustrate the GCA-cortical change relationships, and to characterize consistency of effects across samples (UKB and Lifebrain), we plotted the generalized additive mixed model (GAMM) for the different GCA quintiles, from lowest to highest (Fig. 3), depicting change trajectories for average cortical volume and thickness within the regions showing significant GCA x time associations. Across samples, subgroups with higher GCA started with higher volume and had less volume loss over time. For instance, on average, people with maximum cognitive score in UKB are expected start out with a regional average cortical volume of 1.72 mm3 that would be maintained for the next three years, whereas those with the lowest GCA would on average start out with 1.64 mm3 and decrease to 1.61 mm3 over the next three years. Thus, the greatest GCA-associated differences in cortical volume are found in the intercepts (level), whereas differences in slope (change) are smaller in the follow-up period. For cortical thickness, the change trajectories were also very consistently ordered, but those with higher GCA did not uniformly have thicker cortex at first timepoint in these areas. Rather, differential rates of cortical thinning over time were critical in creating cortical thickness differences in these regions in aging. This was evident in both samples, but especially pronounced in UKB.

Figure 3
figure 3

Cortical change trajectories according to general cognitive ability (GCA). Trajectories are shown per quintile of GCA for illustrative purposes, for mean cortical volume and thickness change in the analysis model and regional significant sites of associations in each cohort (shown in Supplementary Fig. 5). For UKB, the quintiles refer to actual scores from min to max (0–13) on the test, whereas for Lifebrain, the quintiles refer to z-scores (where mean is zero) min to max for the samples.

To further assess effect sizes, we created histograms of the vertex-wise distribution of effects of one SD higher GCA for each metric for absolute volume, area and thickness as well as their change, shown in Fig. 4. (For cortical distributions of such effect sizes per sample, see Supplementary Fig. 7). As can be seen, almost all vertices show positive level–level relationships between GCA and volume and area. For thickness, the distribution is only slightly shifted to the right of zero, confirming the weak GCA-thickness relationships. As for GCA level-brain change, the histograms showed that for area, effects were distributed almost perfectly around zero. For volume, there was a clear shift rightwards, meaning that higher GCA tended to be related to less volume reductions, but substantially less than for the offset effects. Cortical thickness showed the most rightward skewness of the distribution, much larger than for the offset results. Inspecting all histograms, it is clear that higher GCA is related to larger cortical volume and area, and less thickness change.

Figure 4
figure 4

Cortical vertex-wise distributions of effects of 1 SD higher GCA in percent and natural units. Upper panel: effect distributions for level-level associations. Lower panel: effect distributions for level-change associations.

Influence of polygenic scores (PGSs) for GCA and education on the level-level and level-change associations

Next, we investigated whether effects were maintained when covarying for established PGSs for GCA and education in the UKB23,24. Fifty-two participants in the main models were excluded due to missing genetic data. In these analyses, we regressed out the first ten genetic ancestry factors (GAFs) from the GCA variable prior to analysis. The intercept associations of GCA and cortical characteristics that were observed in the main model (Fig. 1) largely remained when controlling for the PGSs, but the extent of the significant regions were somewhat reduced for cortical volume and area (Supplementary Fig. 8). The associations of GCA and cortical change largely remained and were only slightly attenuated when controlling for PGSs for GCA and education (Fig. 5; compare to UKB results in Supplementary Fig. 5).

Figure 5
figure 5

P-value maps of associations of general cognitive ability (GCA) and change in cortical characteristics in the UKB, controlled for the effect of education and polygenic scores (PGSs) for education and GCA over time. The significant regions are shown for the interaction of GCA at baseline (with genetic ancestry factors regressed out) and time (interval from baseline scan), when age, sex, time, GCA, education, and the interactions of education by time, and PGSs by time, are controlled for (p < 0.01, corrected using a cluster-forming threshold of p < 0.01). Regions are shown, from left to right for each panel: right and left lateral view, right and left medial view. No significant regions were seen for cortical area.

Influence of age on the level-change associations

We next tested the three-way interaction baseline GCA × baseline age × time, to see whether the level-change associations differed reliably across the lifespan. Significant interaction effects were seen for change in small regions of the left hemisphere, mostly laterally for volume, and for slightly more extended regions, for cortical thickness (see Fig. 6).

Figure 6
figure 6

Meta-analytic cluster p-value maps for interactions of level general cognitive ability (GCA), by baseline age by time on cortical volume and thickness. The significant regions are shown for the interaction of GCA at baseline, by age at baseline by time (interval since baseline scan), when age, sex, time, GCA, education, and the interactions education by time, GCA by time, and baseline age by time, are controlled for (p < 0.05, corrected using a cluster-forming p-value threshold of p < 0.01). Significant regions are shown, for each panel for left lateral and medial view. No significant regions were seen for cortical area, or for the right hemisphere.

The positive three-way interactions of baseline GCA × baseline age × time indicates that higher level of GCA is associated with less atrophy at distinct ages. To visualize these interaction effects, we divided the cohorts according to whether participants were above or below age 60 years. This division point was chosen in view of it being an approximate age at which select cognitive and regional cortical volume and thickness changes have been reported to accelerate in longitudinal studies27,28. In order to explore how GCA level related to cortical thickness change over time across the two age groups, we plotted the expected cortical change trajectories, within the significant regions shown in Fig. 6, as a function of GCA, with each sample divided into quintiles, from lowest to highest GCA. The plots are shown in Fig. 7. While GCA level was weakly, and in Lifebrain even inversely related to atrophy in these regions in the younger group, the expected trajectories for the older group were relatively consistently ordered so that persons with a higher GCA level had less decline, especially of cortical thickness. The GCA quintile differences are more pronounced in the older group, suggesting the latter half of the lifespan is driving the interaction. As one outlier in the older group in Lifebrain was noted as having a high cortical thickness value for age at the first timepoint in the region of interest, we carefully checked this segmentation, but found no sign of flawed segmentation, and thus decided to keep this person in analyses.

Figure 7
figure 7

Cortical volume and thickness change trajectories according to general cognitive ability (GCA) for young and older adults. For illustrative purposes, expected trajectories are shown for young (< 60 years at baseline) and older (> 60 years at baseline) in UKB (top panels) and Lifebrain (lower panels) per quintile of GCA, for mean cortical volume and thickness in the analysis model and regional significant sites shown in Fig. 6. For UKB, the quintiles refer to actual scores from min to max (0–13) on the test, whereas for Lifebrain, the quintiles refer to z-scores (where mean is zero) min to max for the samples.

Discussion

The current study provides novel findings on GCA not only as a marker of brain characteristics, but also of brain changes in healthy aging. The finding that higher GCA level is associated with larger neuroanatomical structures to begin with, i.e. greater brain reserve, confirms findings in previous studies of various age groups1,2,3,4,25,29. While level of GCA has been associated with cortical change in some older groups10,11, but not others9, the current demonstration of an association of GCA levels, controlled for education, on cortical volume and -thickness declines through the lifespan in multiple cohorts across a relatively long follow-up time, constitutes a novel finding. Also, the finding of an age-interaction with pronounced effects of GCA on cortical thinning and volume changes only in older ages in select regions, is novel.

The association of GCA level and cortical change appears relatively moderate. This may explain why such associations have not previously been consistently found. The “effect” of GCA on cortical change must be viewed in relation to the intercept effects, which, as shown here, constitute a major source of GCA-related cortical volume variation through the lifespan: Those with higher GCA have greater cortical area to begin with, yielding higher cortical volumes in young adulthood. We have previously found that cortical area seems in part determined neuro-developmentally early on, is associated with GCA, and shows parallel trajectories for higher and lower GCA groups1. As there is a relatively minor age change in area, compared to thickness28, slope effects on cortical volume are chiefly caused by moderately different rates of cortical thinning for people of differential cognitive ability. Differences in cortical thinning are thus key to the maintenance effects of GCA, whereas early differences in cortical area drive the intercept effect. Through the adult lifespan, both will affect cortical volume.

It is of interest that these GCA-brain change associations were found when education was controlled for, suggesting that the contemporary GCA level may not only be related to brain reserve16 to begin with, and preserved differentiation18, but also brain maintenance17, and differential preservation18. This is evident from the—across samples—consistently steeper slopes of regional cortical decline with lower GCA (as illustrated in Fig. 3). With our recent findings on the variable nature of education-brain-cognition relationships, as well as education not being associated with atrophy rates in aging25,30, this points to the component of GCA not being associated with education variance as a more promising candidate for predictive or potentially protective effects on brain aging. There is evidence that education may serve to increase GCA31,32. However, while GCA level may be impacted, slope, i.e. cognitive decline, is likely not32,33. There is also evidence to suggest that education, without mediation through adult socioeconomic position, cannot be considered a modifiable risk factor for dementia34.

While one would then think the underlying mechanism in the observed GCA-brain change relationships may be genetic, known genetic factors only partially explained relationships, as effects remained after controlling for PGSs for general cognitive ability and education. However, the PGSs are known to be only moderately predictive of GCA23, and genetic pleiotropic effects on GCA and cortical characteristics and their change may still likely be part of the underlying mechanism. While it has been suggested that GCA may associate with differences in epigenetic age acceleration, it was recently reported that such epigenetic markers did not show associations with longitudinal phenotypic health change35. While it is possible that individual differences in epigenetic age acceleration in older age could be caused by e.g. behaviors associated with intelligence differences over the life course, differences in epigenetic markers and GCA could also both be the result of a shared genetic architecture or some early, including in-utero, environmental event35,36.

A significant three-way interaction of baseline GCA by baseline age by time on regional cortical thickness changes was observed by meta-analysis across cohorts. These effects indicated that higher level of GCA is more associated with less atrophy at older ages. However, as these regional interaction effects were highly restricted, and also seemed to rest in part on unexpected, albeit weakly, inverse direction of smaller effects in younger28 age in Lifebrain, we consider them tentative until replicated. The higher baseline age of the UKB sample, also may make it less suitable to study adult lifespan interactions. Moreover, greater power would be desired to study three-way interactions of possibly smaller effect size.

Some further limitations to the present study should also be noted: The samples included are heterogeneous and may have varying degrees of representativeness of the populations of origin, indeed, lack of population representativity is known37. Data from relatively short time periods were used. Changing exposure trends over time, in health, education, and welfare, may thus relate to age at baseline, and could have effects that could not readily be studied in isolation here. Furthermore, as cognitively healthy participants were recruited, sample representativity may vary with age. Since persons with known neurodegenerative disorders were excluded, results cannot readily be generalized to persons suffering from various types of dementia. To shed light on the potential genetic contribution to the observed GCA-cortical change relationships, we controlled for PGSs for GCA and educational attainment. While these results indicated negligible genetic contributions, direct investigation of the genetic relations using standard methods, e.g. linkage disequilibrium score regression38, may be better suited to investigate this, when large-scale GWAS for longitudinal cortical changes in adulthood becomes available. Finally, change-change relationships between GCA and cortical characteristics could not readily be addressed in the present samples with similar models, due to variability in availability of comparable test data across timepoints. In a lifespan perspective, we know that such relationships do exist, in that both brain and cognition increase in development and decline in aging17,27,28,39. However, to what extent individual differences in GCA change are related to individual differences in cortical trajectories in the present samples, is beyond the scope of this study.

In conclusion, the present study shows that with higher GCA, primarily brain reserve, but also brain maintenance yield higher cortical volumes through the adult lifespan. These effects were seen when controlling for effects of education. As there is otherwise scarce evidence so far that human behavioral traits are associated with differential brain aging trajectories, this is of great interest to investigate further. While controlling for known polygenetic markers for GCA and education did not substantially diminish the effects, the underlying mechanisms may still be related to genetic pleiotropy. However, this leaves open the possibility that factors associated with increased GCA other than education, and possibly genes, could serve to diminish cortical atrophy in aging. Such factors affecting normal individual differences in GCA are not known with certainty, but as childhood GCA is highly predictive of GCA in aging40, they likely work at developmental, rather than adult and senescent stages.

Materials and methods

The UK Biobank (UKB)22 and the Lifebrain samples are described in Table 1. The samples from the European Lifebrain (LB) project (http://www.lifebrain.uio.no/)20 included participants from major European brain studies: the Berlin Study of Aging II (BASE II)41, the BETULA project27, the Cambridge Centre for Ageing and Neuroscience study (Cam-CAN)42, Center for Lifebrain Changes in Brain and Cognition longitudinal studies (LCBC)1, and the University of Barcelona brain studies (UB)43,44,45.

Table 1 Overview of sample characteristics of included cohorts.

GCA was measured by partially different tests in the different cohorts. National versions of a series of batteries and tests were used, see SM for details. These included the UKB Fluid Intelligence test46, tests from the Wechsler batteries47,48,49 combined with the National Adult Reading Test (NART)50, the Cattell Culture Fair Test51 combined with the Spot The Word task52, as well as local batteries, for which procedures are described in SM and elsewhere53,54. It is clearly a limitation that content and reliability of the GCA measures may vary, but there is reason to assume that the measures index partally similar abilities. For instance, the UKB fluid intelligence measure has been shown to have moderate to high reliability, and correlated > 0.50 with a measure of GCA created using 11 reference tests, including NART and Wechsler measures55. See SM for further details.

MRIs were processed using FreeSurfer, version 7.1 for Lifebrain, and version 6.0 for UKB (https://surfer.nmr.mgh.harvard.edu/https://surfer.nmr.mgh.harvard.edu)56,57,58,59. We ran vertex-wise analyses to assess regional variation in the relationships between cortical structure and the measures of interest, i.e. GCA and the interaction of GCA × time. Cortical surfaces were reconstructed from the same T1-weighted anatomical MRIs, yielding maps of cortical area, thickness and volume. Surfaces were smoothed with a Gaussian kernel of 15 mm full-width at half-maximum. Spatiotemporal linear mixed models60,61 were performed running on MATLAB R2017a (using FreeSurfers ST-LME package https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels), for each of the samples separately, with GCA, and then additionally with the interaction term of GCA and time in turn as predictors, and sex, baseline age, scanner, time (interval since baseline scan) and education were entered as covariates unless otherwise noted. These models also account for the spatial correlation between residuals at neighboring vertices and the temporal correlation of residuals within repeated measurements of single participants. Surface results were tested against an empirical null distribution of maximum cluster size across 10 000 iterations using Z Monte Carlo simulations, yielding results corrected for multiple comparisons across space (p < 0.01 corrected)62.

All studies were conducted, and all methods performed, in accordance with relevant guidelines and regulations as set forth by the relevant authorities, including the Declaration of Helsinki, all participants gave informed consent, and subprojects were approved by the relevant ethical review boards. UK Biobank has approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank approval. The Lifebrain project was approved by Regional Committees for Medical Research Ethics–South East Norway. For additional details, see SM. Screening criteria were not identical across studies, but participants were recruited to be cognitively healthy and did not suffer from neurological conditions known to affect brain function, such as dementia. All samples consisted of community-dwelling participants, some were convenience samples, whereas others were contacted on the basis of population registry information. Further details on samples, GCA measures, MRI acquisition and processing and statistical analyses, are presented in SM. The Lifebrain data supporting the results of the current study are available from the PI of each sub-study on request (see SM), given approvals. UK Biobank data requests can be submitted to http://www.ukbiobank.ac.uk. Computer code used for the analyses is available on github: https://github.com/Lifebrain/p032-gca-brain-change.

References

  1. Walhovd, K. B. et al. Neurodevelopmental origins of lifespan changes in brain and cognition. Proc. Natl. Acad. Sci. USA. 113, 9357–9362. https://doi.org/10.1073/pnas.1524259113 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  2. Fjell, A. M. et al. High-expanding cortical regions in human development and evolution are related to higher intellectual abilities. Cereb. Cortex 25, 26–34. https://doi.org/10.1093/cercor/bht201 (2015).

    Article 
    PubMed 

    Google Scholar
     

  3. Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M. & Deary, I. J. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence 76, 101376. https://doi.org/10.1016/j.intell.2019.101376 (2019).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  4. Deary, I. J., Hill, W. D. & Gale, C. R. Intelligence, health and death. Nat. Hum. Behav. 5, 416–430. https://doi.org/10.1038/s41562-021-01078-9 (2021).

    Article 
    PubMed 

    Google Scholar
     

  5. Deary, I. J., Cox, S. R. & Hill, W. D. Genetic variation, brain, and intelligence differences. Mol. Psychiatry https://doi.org/10.1038/s41380-021-01027-y (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  6. Livingston, G. et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396, 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  7. Livingston, G. et al. Dementia prevention, intervention, and care. Lancet 390, 2673–2734. https://doi.org/10.1016/S0140-6736(17)31363-6 (2017).

    Article 
    PubMed 

    Google Scholar
     

  8. Nyberg, L. et al. Educational attainment does not influence brain aging. Proc. Natl. Acad. Sci. USA 118, 18. https://doi.org/10.1073/pnas.2101644118 (2021).

    CAS 
    Article 

    Google Scholar
     

  9. Jancke, L., Sele, S., Liem, F., Oschwald, J. & Merillat, S. Brain aging and psychometric intelligence: A longitudinal study. Brain Struct. Funct. 225, 519–536. https://doi.org/10.1007/s00429-019-02005-5 (2020).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  10. Ritchie, S. J. et al. Brain volumetric changes and cognitive ageing during the eighth decade of life. Hum. Brain Mapp. 36, 4910–4925. https://doi.org/10.1002/hbm.22959 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  11. Raz, N. et al. Neuroanatomical correlates of fluid intelligence in healthy adults and persons with vascular risk factors. Cereb. Cortex 18, 718–726. https://doi.org/10.1093/cercor/bhm108 (2008).

    Article 
    PubMed 

    Google Scholar
     

  12. Yeo, R. A., Arden, R. & Jung, R. E. Alzheimer’s disease and intelligence. Curr. Alzheimer Res. 8, 345–353. https://doi.org/10.2174/156720511795745276 (2011).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  13. Nyberg, J. et al. Cardiovascular and cognitive fitness at age 18 and risk of early-onset dementia. Brain 137, 1514–1523. https://doi.org/10.1093/brain/awu041 (2014).

    Article 
    PubMed 

    Google Scholar
     

  14. Talboom, J. S. et al. Family history of Alzheimer’s disease alters cognition and is modified by medical and genetic factors. Elife 8, 4619. https://doi.org/10.7554/eLife.46179 (2019).

    Article 

    Google Scholar
     

  15. Rodriguez, F. S. & Lachmann, T. Systematic review on the impact of intelligence on cognitive decline and dementia risk. Front. Psychiatry 11, 658. https://doi.org/10.3389/fpsyt.2020.00658 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  16. Katzman, R. et al. Clinical, pathological, and neurochemical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques. Ann. Neurol. 23, 138–144. https://doi.org/10.1002/ana.410230206 (1988).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  17. Nyberg, L., Lovden, M., Riklund, K., Lindenberger, U. & Backman, L. Memory aging and brain maintenance. Trends Cogn. Sci. 16, 292–305. https://doi.org/10.1016/j.tics.2012.04.005 (2012).

    Article 
    PubMed 

    Google Scholar
     

  18. Salthouse, T. A., Babcock, R. L., Skovronek, E., Mitchell, D. R. D. & Palmon, R. Age and experience effects in spatial visualization. Dev. Psychol. 26, 128–136. https://doi.org/10.1037/0012-1649.26.1.128 (1990).

    Article 

    Google Scholar
     

  19. Tucker-Drob, E. M. Cognitive aging and dementia: A life span perspective. Annu. Rev. Dev. Psychol. 1, 177–196. https://doi.org/10.1146/annurev-devpsych-121318-085204 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  20. Walhovd, K. B. et al. Healthy minds 0–100 years: Optimising the use of European brain imaging cohorts (“Lifebrain”). Eur. Psychiatry 50, 47–56. https://doi.org/10.1016/j.eurpsy.2017.12.006 (2018).

    Article 
    PubMed 

    Google Scholar
     

  21. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424. https://doi.org/10.1016/j.neuroimage.2017.10.034 (2018).

    Article 
    PubMed 

    Google Scholar
     

  22. Nobis, L. et al. Hippocampal volume across age: Nomograms derived from over 19,700 people in UK Biobank. Neuroimage Clin. 23, 101904. https://doi.org/10.1016/j.nicl.2019.101904 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  23. Hill, W. D. et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol. Psychiatry 24, 169–181. https://doi.org/10.1038/s41380-017-0001-5 (2019).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  24. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121. https://doi.org/10.1038/s41588-018-0147-3 (2018).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  25. Walhovd, K. B. et al. Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts. Cereb. Cortex https://doi.org/10.1093/cercor/bhab248 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  26. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48. https://doi.org/10.18637/jss.v036.i03 (2010).

    Article 

    Google Scholar
     

  27. Nyberg, L. et al. Biological and environmental predictors of heterogeneity in neurocognitive ageing: Evidence from Betula and other longitudinal studies. Ageing Res. Rev. 64, 101184. https://doi.org/10.1016/j.arr.2020.101184 (2020).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  28. Storsve, A. B. et al. Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: Regions of accelerating and decelerating change. J. Neurosci. 34, 8488–8498. https://doi.org/10.1523/JNEUROSCI.0391-14.2014 (2014).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  29. Walhovd, K. B. et al. Cortical volume and speed-of-processing are complementary in prediction of performance intelligence. Neuropsychologia 43, 704–713. https://doi.org/10.1016/j.neuropsychologia.2004.08.006 (2005).

    Article 
    PubMed 

    Google Scholar
     

  30. Raz, N., Rodrigue, K. M., Kennedy, K. M. & Acker, J. D. Vascular health and longitudinal changes in brain and cognition in middle-aged and older adults. Neuropsychology 21, 149–157. https://doi.org/10.1037/0894-4105.21.2.149 (2007).

    Article 
    PubMed 

    Google Scholar
     

  31. Lager, A., Seblova, D., Falkstedt, D. & Lovden, M. Cognitive and emotional outcomes after prolonged education: A quasi-experiment on 320 182 Swedish boys. Int. J. Epidemiol. 46, 303–311. https://doi.org/10.1093/ije/dyw093 (2017).

    Article 
    PubMed 

    Google Scholar
     

  32. Lovden, M., Fratiglioni, L., Glymour, M. M., Lindenberger, U. & Tucker-Drob, E. M. Education and cognitive functioning across the life span. Psychol. Sci. Public Interest 21, 6–41. https://doi.org/10.1177/1529100620920576 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  33. Seblova, D., Berggren, R. & Lovden, M. Education and age-related decline in cognitive performance: Systematic review and meta-analysis of longitudinal cohort studies. Ageing Res. Rev. 58, 101005. https://doi.org/10.1016/j.arr.2019.101005 (2020).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  34. Seblova, D. et al. Does prolonged education causally affect dementia risk when adult socioeconomic status is not altered? A Swedish natural experiment in 1.3 million individuals. Am. J. Epidemiol. 190, 817–826. https://doi.org/10.1093/aje/kwaa255 (2021).

    Article 
    PubMed 

    Google Scholar
     

  35. Stevenson, A. J. et al. Childhood intelligence attenuates the association between biological ageing and health outcomes in later life. Transl. Psychiatry 9, 323. https://doi.org/10.1038/s41398-019-0657-5 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  36. Li, S. et al. Genome-wide average DNA methylation is determined in utero. Int. J. Epidemiol. 47, 908–916. https://doi.org/10.1093/ije/dyy028 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  37. Stamatakis, E. et al. Is cohort representativeness passe? Poststratified Associations of lifestyle risk factors with mortality in the UK biobank. Epidemiology 32, 179–188. https://doi.org/10.1097/EDE.0000000000001316 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  38. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295. https://doi.org/10.1038/ng.3211 (2015).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  39. Walhovd, K. B. et al. Long-term influence of normal variation in neonatal characteristics on human brain development. Proc. Natl. Acad. Sci. U.S.A. 109, 20089–20094. https://doi.org/10.1073/pnas.1208180109 (2012).

    ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  40. Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R. & Starr, J. M. The stability of individual differences in mental ability from childhood to old age: Follow-up of the 1932 Scottish Mental Survey. Intelligence 28, 49–55. https://doi.org/10.1016/S0160-2896%2899%2900031-8 (2000).

    Article 

    Google Scholar
     

  41. Bertram, L. et al. Cohort profile: The Berlin aging study II (BASE-II). Int. J. Epidemiol. 43, 703–712. https://doi.org/10.1093/ije/dyt018 (2014).

    Article 
    PubMed 

    Google Scholar
     

  42. Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: A cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14, 204. https://doi.org/10.1186/s12883-014-0204-1 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  43. Rajaram, S. et al. The Walnuts and Healthy Aging Study (WAHA): Protocol for a nutritional intervention trial with walnuts on brain aging. Front. Aging Neurosci. 8, 333. https://doi.org/10.3389/fnagi.2016.00333 (2016).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  44. Uribe, C. et al. Patterns of cortical thinning in nondemented Parkinson’s disease patients. Movement Disord. 31, 699–708. https://doi.org/10.1002/mds.26590 (2016).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  45. Vidal-Pineiro, D. et al. Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging. Brain Stimul. 7, 287–296. https://doi.org/10.1016/j.brs.2013.12.016 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  46. Sudlow, C. et al. UK biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779. https://doi.org/10.1371/journal.pmed.1001779 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  47. Wechsler, D. Wchsler Abbreviated Scale of Intelligence (The Psychological Corporation, 1999).


    Google Scholar
     

  48. Wechsler, D. Wechsler Adult Intelligence Scale (WAIS-III): Administration and Scoring Manual 3rd edn. (The Psychological Corporation, 1997).


    Google Scholar
     

  49. Wechsler, D. Wechsler Adult Intelligence Scale: Fourth Edition (WAIS-IV) (Pearson Education Inc., 2008).


    Google Scholar
     

  50. Nelson, H. & Willison, J. The National Adult Reading Test (NART) (Windsor NFER-Nelson, 1991).


    Google Scholar
     

  51. Cattell, R. B. & Cattell, H. E. P. Measuring Intelligence with the Culture Fair Tests (The Institute for Personality and Ability Testing, 1973).


    Google Scholar
     

  52. Baddeley, A., Emslie, H. & Nimmosmith, I. The spot-the-word test: A robust estimate of verbal intelligence based on lexical decision. Br. J. Clin. Psychol. 32, 55–65. https://doi.org/10.1111/j.2044-8260.1993.tb01027.x (1993).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  53. Duzel, S. et al. The subjective health horizon questionnaire (SHH-Q): Assessing future time perspectives for facets of an active lifestyle. Gerontology 62, 345–353. https://doi.org/10.1159/000441493 (2016).

    Article 
    PubMed 

    Google Scholar
     

  54. Nilsson, L. G. et al. The Betula prospective cohort study: Memory, health, and aging. Aging Neuropsychol. Cogn. 4, 1–32. https://doi.org/10.1080/13825589708256633 (1997).

    Article 

    Google Scholar
     

  55. Fawns-Ritchie, C. & Deary, I. J. Reliability and validity of the UK Biobank cognitive tests. PLoS ONE 15, e0231627. https://doi.org/10.1371/journal.pone.0231627 (2020).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  56. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis I. Segmentation and surface reconstruction. Neuroimage 9, 179–194. https://doi.org/10.1006/nimg.1998.0395 (1999).

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  57. Fischl, B. et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    CAS 
    Article 

    Google Scholar
     

  58. Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418. https://doi.org/10.1016/j.neuroimage.2012.02.084 (2012).

    Article 
    PubMed 

    Google Scholar
     

  59. Jovicich, J. et al. Brain morphometry reproducibility in multi-center 3T MRI studies: A comparison of cross-sectional and longitudinal segmentations. Neuroimage 83, 472–484. https://doi.org/10.1016/j.neuroimage.2013.05.007 (2013).

    Article 
    PubMed 

    Google Scholar
     

  60. Bernal-Rusiel, J. L. et al. Statistical analysis of longitudinal neuroimage data with linear mixed effects models. Neuroimage 66, 249–260. https://doi.org/10.1016/j.neuroimage.2012.10.065 (2013).

    Article 
    PubMed 

    Google Scholar
     

  61. Bernal-Rusiel, J. L. et al. Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Neuroimage 81, 358–370. https://doi.org/10.1016/j.neuroimage.2013.05.049 (2013).

    Article 
    PubMed 

    Google Scholar
     

  62. Hagler, D. J. Jr., Saygin, A. P. & Sereno, M. I. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. Neuroimage 33, 1093–1103. https://doi.org/10.1016/j.neuroimage.2006.07.036 (2006).

    Article 
    PubMed 

    Google Scholar
     

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Acknowledgements

The Lifebrain project is funded by the EU Horizon 2020 Grant Agreement Number 732592 (Lifebrain). In addition, the different sub-studies are supported by different sources: LCBC: The European Research Council under grant agreements 283634, 725025 (to A.M.F.) and 313440 (to K.B.W.), as well as the Norwegian Research Council (to A.M.F., K.B.W.), The National Association for Public Health’s dementia research program, Norway (to A.M.F). Betula: a scholar grant from the Knut and Alice Wallenberg (KAW) foundation to L.N. Barcelona: Partially supported by an ICREA Academia 2019 grant award; by the California Walnut Commission, Sacramento, California. BASE-II has been supported by the German Federal Ministry of Education and Research under Grant Numbers 16SV5537/ 16SV5837/ 16SV5538/ 16SV5536K /01UW0808/ 01UW0706/ 01GL1716A/ 01GL1716B, the European Research Council under grant agreement 677804 (to S.K.). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) was supported by a programme grant from the UK Biotechnology and Biological Sciences Research Council (Grant Number BB/H008217/1) and by continued intramural funding from the UK Medical Research Council to the Cognition & Brain Sciences Unit in Cambridge. Part of the research was conducted using the UK Biobank resource under Application Number 32048.

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K.B.W. drafted the manuscript, after critical discussions of the research questions tested with L.N., U.L. and A.M.F. I.K.A., Ø.S., Y.W., A.M.M., D.V.-P. and F.M., contributed to management, processing and analyses of data. U.L., K.B.W., L.N., A.M.F., D.B.-F. contributed to make data from the Lifebrain cohorts available. All authors, including R.A.K., K.P.E., S.K., C.-J.B., P.G., K.S.M., W.F.C.B., E.Z., and B.P., contributed to reviewing and revising the manuscript draft.

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Walhovd, K.B., Nyberg, L., Lindenberger, U. et al. Brain aging differs with cognitive ability regardless of education.
Sci Rep 12, 13886 (2022). https://doi.org/10.1038/s41598-022-17727-6

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  • Received: 04 March 2022

  • Accepted: 29 July 2022

  • Published: 16 August 2022

  • DOI: https://doi.org/10.1038/s41598-022-17727-6

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