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The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 −8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine . The biological basis of brain aging is not well understood, but it has implications for human health. Here, the authors explore the genetic basis of human brain aging, finding genetic variants, genes and potential causal relationships with disease.

Mind body therapies, particularly yoga, may offer a simple, scalable, and effective intervention for prolonged grief disorder (PGD) in older adults. This pilot randomized controlled trial (RCT) compared Iyengar Yoga (IY) with Health Education (HE), an active control, assessing feasibility, safety, and clinical effects in those with probable PGD in later life. Thirty-nine bereaved adults (median age: 62.0 years) with probable PGD were randomized to IY (n = 19) or HE (n = 20). Participants attended 60-min weekly group sessions for 10 weeks. Feasibility, safety, IY instructor fidelity, and grief and depressive symptoms were assessed over time, with effect sizes (Hedges’ g) calculated to explore within-group and between-group clinical changes. Retention was 84.6%, with 100% of participants attending at least one session. Among completers (n = 33), 90.9% attended at least 80% of sessions. IY completers submitted an average of 8.65 weekly homework logs, and 82% practiced yoga at home for 60 + minutes weekly. All randomly selected IY classes met instructor fidelity. Participants in both groups reported high satisfaction; IY participants endorsed greater perceived benefits. No serious adverse events occurred. Both groups showed clinical improvements, with mean effect sizes ranging from 0.69 to 1.28 (medium to large) for overall grief and depressive symptoms, and 0.28 to 0.88 (small to large) for grief-specific symptoms. Between-group effect sizes for clinical outcomes were small. An IY and HE group intervention trial is feasible, safe, and acceptable for adults with PGD in later life. A larger, adequately powered RCT is needed to establish clinical efficacy of these interventions for late-life PGD. Trial registration . The trial is registered at ClinicalTrials.gov (NCT05026827, 08/30/2021).

We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low‐frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically‐influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low‐level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long‐term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high‐level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex‐specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task‐oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5‐19.1 times more GIF connections in males than females. These preliminary (young adult cohort‐specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex‐specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort‐specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain‐wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.] This work investigated genetic influence on low‐frequency‐oscillation‐based functional connectivity in young males and female adults using a minimal assumption method (compared with standard ACE/ADE model approaches) with 65 monozygotic and 65 dizygotic twins' resting state functional MRI data. There was a high number of genetically‐influenced posterior–posterior (visual/parietal/temporal) brain region functional connections, a low number of genetically‐influenced posterior/anterior brain region functional connections, and a very low number across noncortical/subcortical regions at the population level. Males had greater genetic influence interplay across eDMN and TPNs than females, and had over 8.5‐19.1 times more genetically influenced functional connections than females (with similar, although different proportionate trends across nonadditive genetics in the ACE/ADE model). These preliminary (young adult cohort specific) findings may suggest sex‐specific individual differences in the functional connectivity of the resting state brain in terms of genetic influence in young adults. Furthermore, by employing the ACE/ADE model, it may be that environmental influences on individual differences are substantially greater than that of genetics, regardless of sex, notably frontally but brain‐wide in general.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups—a ‘lower brain volume’ subgroup (SG1) and an ‘higher striatal volume’ subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership (‘None’), and mixed SG1 + SG2 subgroups (‘Mixed’). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of ‘lower brain volume’ in SG1 and ‘higher striatal volume’ (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p  < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Prolonged grief disorder (PGD) is associated with impairments in cognitive functioning, but the neuropsychological correlates of early grief in older adults are poorly understood. This preliminary study cross-sectionally examined neuropsychological functioning in bereaved adults with high and low grief symptoms and a non-bereaved comparison sample and further explored the relationship between multidomain cognitive measures and grief severity. A total of ninety-three nondemented older adults (high grief: n = 44; low grief: n = 49) within 12 months post-bereavement and non-bereaved comparison participants (n = 43) completed neuropsychological battery including global and multiple domain-specific cognitive functioning. Linear regression models were used to analyze differences in multidomain cognitive measures between the groups and specifically examine the associations between cognitive performance and grief severity in the bereaved, after covariate adjustment, including depressive symptoms. Bereaved older adults with higher grief symptoms performed worse than those with lower symptoms and non-bereaved participants on executive functioning and attention and processing speed measures. In the bereaved, poorer executive functioning, attention and processing speed correlated with higher grief severity. Attention/processing speed–grief severity correlation was seen in those with time since loss ≤ 6 months, but not > 6 months. Intense early grief is characterised by poorer executive functioning, attention, and processing speed, resembling findings in PGD. The putative role of poorer cognitive functioning during early grief on the transition to integrated grief or the development of PGD remains to be elucidated.

INTRODUCTION Variability in relationship of tau‐based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non‐specific nature of N, modulated by non‐AD co‐pathologies, age‐related changes, and resilience factors. METHODS We used regional T‐N residual patterns to partition 184 patients within the Alzheimer's continuum into data‐driven groups. These were compared with groups from 159 non‐AD (amyloid “negative”) patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T‐N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS AD groups displayed spatial T‐N mismatch patterns resembling neurodegeneration patterns in non‐AD groups, similarly associated with non‐AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T‐N mismatch correlated with TDP‐43 co‐pathology. DISCUSSION T‐N mismatch may provide a personalized approach for determining non‐AD factors associated with resilience/vulnerability in AD.

Machine learning is changing the field of medical imaging. Studying complex neurological diseases like epilepsy can substantially benefit from its use. It can offer valuable insight onto the disease characteristics and also train predictive models to be used in various applications. Using both imaging and neuropsychological data provided by the Epilepsy Connectome Project, this work explores using machine learning to study temporal lobe epilepsy population in three steps. First, it exploits the feature extraction ability of machine learning to find that the frequency range between 0.1 – 0.073Hz is best at capturing abnormal resting-state functional connectivity in temporal lobe epilepsy compared to healthy controls, and that the impaired processing speed is the most informative among other neuropsychological tests in separating between the two groups. Second, it builds machine learning classification and regression models that can make various predictions on temporal lobe epilepsy patients. One finding reveals that temporal lobe epilepsy patients exhibit functional brains that are predicted to be on average 8.3 years older compared to their chronological ages. Third, the relationship between the sample size and binary classification accuracy is systematically explored using neuroimaging data. A number of guidelines are proposed for future research, as well as an equation for the sample size relationship that can be used to predict future accuracies given limited samples. Finally, it ends with suggestions of future research directions. Overall, this work presents how machine learning can facilitate epilepsy research and suggests ways that the limited sample size problems can be addressed.

Alzheimer’s disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the “diffuse-AD” (R1) dimension shows widespread brain atrophy, and the “MTL-AD” (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4 ) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were “druggable genes” for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4 , amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction—driven by genes different from APOE —which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with left TLE compared with healthy controls was investigated using spectral dynamic causal modeling (spDCM) of resting-state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spDCM. Two different second-level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN, which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.