Benzodiazepine use in relation to long-term dementia risk and imaging markers of neurodegeneration: a population-based study.

BACKGROUND: Benzodiazepine use is common, particularly in older adults. Benzodiazepines have well-established acute adverse effects on cognition, but long-term effects on neurodegeneration and dementia risk remain uncertain. METHODS: We included 5443 cognitively healthy (MMSE ≥ 26) participants from the population-based Rotterdam Study (57.4% women, mean age 70.6 years). Benzodiazepine use from 1991 until baseline (2005-2008) was derived from pharmacy dispensing records, from which we determined drug type and cumulative dose. Benzodiazepine use was defined as prescription of anxiolytics (ATC-code: N05BA) or sedative-hypnotics (ATC-code: N05CD) between inception of pharmacy records and study baseline. Cumulative dose was calculated as the sum of the defined daily doses for all prescriptions. We determined the association with dementia risk until 2020 using Cox regression. Among 4836 participants with repeated brain MRI, we further determined the association of benzodiazepine use with changes in neuroimaging markers using linear mixed models. RESULTS: Of all 5443 participants, 2697 (49.5%) had used benzodiazepines at any time in the 15 years preceding baseline, of whom 1263 (46.8%) used anxiolytics, 530 (19.7%) sedative-hypnotics, and 904 (33.5%) used both; 345 (12.8%) participants were still using at baseline assessment. During a mean follow-up of 11.2 years, 726 participants (13.3%) developed dementia. Overall, use of benzodiazepines was not associated with dementia risk compared to never use (HR [95% CI]: 1.06 [0.90-1.25]), irrespective of cumulative dose. Risk estimates were somewhat higher for any use of anxiolytics than for sedative-hypnotics (HR 1.17 [0.96-1.41] vs 0.92 [0.70-1.21]), with strongest associations for high cumulative dose of anxiolytics (HR [95% CI] 1.33 [1.04-1.71]). In imaging analyses, current use of benzodiazepine was associated cross-sectionally with lower brain volumes of the hippocampus, amygdala, and thalamus and longitudinally with accelerated volume loss of the hippocampus and to a lesser extent amygdala. However, imaging findings did not differ by type of benzodiazepines or cumulative dose. CONCLUSIONS: In this population-based sample of cognitively healthy adults, overall use of benzodiazepines was not associated with increased dementia risk, but potential class-dependent adverse effects and associations with subclinical markers of neurodegeneration may warrant further investigation.

Gray matter alterations in Huntington's disease: A meta-analysis of VBM neuroimaging studies.

Increasing neuroimaging studies have attempted to identify biomarkers of Huntington's disease (HD) progression. Here, we conducted voxel-based meta-analyses of voxel-based morphometry (VBM) studies on HD to investigate the evolution of gray matter volume (GMV) alterations and explore the effects of genetic and clinical features on GMV changes. A systematic review was performed to identify the relevant studies. Meta-analyses of whole-brain VBM studies were performed to assess the regional GMV changes in all HD mutation carriers, in presymptomatic HD (pre-HD), and in symptomatic HD (sym-HD). A quantitative comparison was performed between pre-HD and sym-HD. Meta-regression analyses were used to explore the effects of genetic and clinical features on GMV changes. Twenty-eight studies were included, comparing a total of 1811 HD mutation carriers [including 1150 pre-HD and 560 sym-HD] and 969 healthy controls (HCs). Pre-HD showed decreased GMV in the bilateral caudate nuclei, putamen, insula, anterior cingulate/paracingulate gyri, middle temporal gyri, and left dorsolateral superior frontal gyrus compared with HCs. Compared with pre-HD, GMV decrease in sym-HD extended to the bilateral median cingulate/paracingulate gyri, Rolandic operculum and middle occipital gyri, left amygdala, and superior temporal gyrus. Meta-regression analyses found that age, mean lengths of CAG repeats, and disease burden were negatively associated with GMV atrophy of the bilateral caudate and right insula in all HD mutation carriers. This meta-analysis revealed the pattern of GMV changes from pre-HD to sym-HD, prompting the understanding of HD progression. The pattern of GMV changes may be biomarkers for disease progression in HD.

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's disease neuroimaging initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.

Neuroimaging biomarkers and CSF sTREM2 levels in Alzheimer's disease: a longitudinal study.

Understanding the exact pathophysiological mechanisms underlying the involvement of triggering receptor expressed on myeloid cells 2 (TREM2) related microglia activation is crucial for the development of clinical trials targeting microglia activation at different stages of Alzheimer's disease (AD). Given the contradictory findings in the literature, it is imperative to investigate the longitudinal alterations in cerebrospinal fluid (CSF) soluble TREM2 (sTREM2) levels as a marker for microglia activation, and its potential association with AD biomarkers, in order to address the current knowledge gap. In this study, we aimed to assess the longitudinal changes in CSF sTREM2 levels within the framework of the A/T/N classification system for AD biomarkers and to explore potential associations with AD pathological features, including the presence of amyloid-beta (Aß) plaques and tau aggregates. The baseline and longitudinal (any available follow-up visit) CSF sTREM2 levels and processed tau-PET and Aß-PET data of 1001 subjects were recruited from the ADNI database. The participants were classified into four groups based on the A/T/N framework: A+ /TN+ , A+ /TN- , A- /TN+ , and A- /TN- . Linear regression analyses were conducted to assess the relationship between CSF sTREM2 with cognitive performance, tau and Aß-PET adjusting for age, gender, education, and APOE ε4 status. Based on our analysis there was a significant difference in baseline and rate of change of CSF sTREM2 between ATN groups. While there was no association between baseline CSF sTREM2 and cognitive performance (ADNI-mem), we found that the rate of change of CSF sTREM2 is significantly associated with cognitive performance in the entire cohort but not the ATN groups. We found that the baseline CSF sTREM2 is significantly associated with baseline tau-PET and Aß-PET rate of change only in the A+ /TN+ group. A significant association was found between the rate of change of CSF sTREM2 and the tau- and Aß-PET rate of change only in the A+ /TN- group. Our study suggests that the TREM2-related microglia activation and their relations with AD markers and cognitive performance vary the in presence or absence of Aß and tau pathology. Furthermore, our findings revealed that a faster increase in the level of CSF sTREM2 might attenuate future Aß plaque formation and tau aggregate accumulation only in the presence of Aß pathology.

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder.

Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightings from brain T1-weighted MRI and topic probabilities from clinical notes. Specifically, we used 5 psychiatric symptom topics and 5 brain networks for models. Anxiety and somatic symptoms topics were significantly more common in the suicidal group, and there were group differences in the default mode and cortical midline networks. The clinical symptoms plus structural brain patterns model had the highest area under the receiver operating characteristic curve (0.794) versus the clinical notes only and brain MRI only models (0.748 and 0.738, respectively). The results were consistent across performance metrics and external validation. Our findings suggest that focusing on personalized neuroimaging and natural language processing variables improves evaluation of suicidal thoughts.

Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.

Neuroimaging and clinical features of bilateral Wallerian degeneration of middle cerebellar peduncles subsequent to pontine infarction.

OBJECTIVE: Wallerian degeneration (WD) of the middle cerebellar peduncles (MCPs) following pontine infarction is a rare secondary degenerative neurological condition. Due to its infrequency, there is limited research on its characteristics. METHODS: This study aims to present three cases of WD of MCPs following pontine infarction and to analyze the prognosis, clinical manifestations, and neuroimaging features by amalgamating our cases with previously reported ones. RESULTS: The cohort consisted of 25 cases, comprising 18 men and 7 women aged 29 to 77 years (mean age: 66.2 years). The majority of patients (94%) exhibit risk factors for cerebrovascular disease, with hypertension being the primary risk factor. Magnetic resonance imaging (MRI) can detect WD of MCPs within a range of 21 days to 12 months following pontine infarction. This degeneration is characterized by bilateral symmetric hyperintensities on T2/FLAIR-weighted images (WI) lesions in the MCPs. Moreover, restricted diffusion, with hyperintensity on diffusion-weighted imaging (DWI) and low apparent diffusion coefficient (ADC) signal intensity may be observed as early as 21 days after the infarction. Upon detection of WD, it was observed that 20 patients (80%) remained asymptomatic during subsequent clinic visits, while four (16%) experienced a worsening of pre-existing symptoms. CONCLUSIONS: These findings underscore the importance of neurologists enhancing their understanding of this condition by gaining fresh insights into the neuroimaging characteristics, clinical manifestations, and prognosis of individuals with WD of bilateral MCPs.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Choroid plexus volume as a novel candidate neuroimaging marker of the Alzheimer's continuum.

BACKGROUND: Enlarged choroid plexus (ChP) volume has been reported in patients with Alzheimer's disease (AD) and inversely correlated with cognitive performance. However, its clinical diagnostic and predictive value, and mechanisms by which ChP impacts the AD continuum remain unclear. METHODS: This prospective cohort study enrolled 607 participants [healthy control (HC): 110, mild cognitive impairment (MCI): 269, AD dementia: 228] from the Chinese Imaging, Biomarkers, and Lifestyle study between January 1, 2021, and December 31, 2022. Of the 497 patients on the AD continuum, 138 underwent lumbar puncture for cerebrospinal fluid (CSF) hallmark testing. The relationships between ChP volume and CSF pathological hallmarks (Aß42, Aß40, Aß42/40, tTau, and pTau181), neuropsychological tests [Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Neuropsychiatric Inventory (NPI), and Activities of Daily Living (ADL) scores], and multimodal neuroimaging measures [gray matter volume, cortical thickness, and corrected cerebral blood flow (cCBF)] were analyzed using partial Spearman's correlation. The mediating effects of four neuroimaging measures [ChP volume, hippocampal volume, lateral ventricular volume (LVV), and entorhinal cortical thickness (ECT)] on the relationship between CSF hallmarks and neuropsychological tests were examined. The ability of the four neuroimaging measures to identify cerebral Aß42 changes or differentiate among patients with AD dementia, MCI and HCs was determined using receiver operating characteristic analysis, and their associations with neuropsychological test scores at baseline were evaluated by linear regression. Longitudinal associations between the rate of change in the four neuroimaging measures and neuropsychological tests scores were evaluated on the AD continuum using generalized linear mixed-effects models. RESULTS: The participants' mean age was 65.99 ± 8.79 years. Patients with AD dementia exhibited the largest baseline ChP volume than the other groups (P < 0.05). ChP volume enlargement correlated with decreased Aß42 and Aß40 levels; lower MMSE and MoCA and higher NPI and ADL scores; and lower volume, cortical thickness, and cCBF in other cognition-related regions (all P < 0.05). ChP volume mediated the association of Aß42 and Aß40 levels with MMSE scores (19.08% and 36.57%), and Aß42 levels mediated the association of ChP volume and MMSE or MoCA scores (39.49% and 34.36%). ChP volume alone better identified cerebral Aß42 changes than LVV alone (AUC = 0.81 vs. 0.67, P = 0.04) and EC thickness alone (AUC = 0.81 vs.0.63, P = 0.01) and better differentiated patients with MCI from HCs than hippocampal volume alone (AUC = 0.85 vs. 0.81, P = 0.01), and LVV alone (AUC = 0.85 vs.0.82, P = 0.03). Combined ChP and hippocampal volumes significantly increased the ability to differentiate cerebral Aß42 changes and patients among AD dementia, MCI, and HCs groups compared with hippocampal volume alone (all P < 0.05). After correcting for age, sex, years of education, APOE ε4 status, eTIV, and hippocampal volume, ChP volume was associated with MMSE, MoCA, NPI, and ADL score at baseline, and rapid ChP volume enlargement was associated with faster deterioration in NPI scores with an average follow-up of 10.03 ± 4.45 months (all P < 0.05). CONCLUSIONS: ChP volume may be a novel neuroimaging marker associated with neurodegenerative changes and clinical AD manifestations. It could better detect the early stages of the AD and predict prognosis, and significantly enhance the differential diagnostic ability of hippocampus on the AD continuum.

Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration.

BACKGROUND: Leveraging Alzheimer's disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR. METHODS: We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aß, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women). RESULTS: The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline. CONCLUSION: These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions.

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury.

BACKGROUND: Mild traumatic brain injury (mTBI) can result in lasting brain damage that is often too subtle to detect by qualitative visual inspection on conventional MR imaging. Although a number of FDA-cleared MR neuroimaging tools have demonstrated changes associated with mTBI, they are still under-utilized in clinical practice. METHODS: We investigated a group of 65 individuals with predominantly mTBI (60 mTBI, 48 due to motor-vehicle collision, mean age 47 ± 13 years, 27 men and 38 women) with MR neuroimaging performed in a median of 37 months post-injury. We evaluated abnormalities in brain volumetry including analysis of left-right asymmetry by quantitative volumetric analysis, cerebral perfusion by pseudo-continuous arterial spin labeling (PCASL), white matter microstructure by diffusion tensor imaging (DTI), and neurometabolites via magnetic resonance spectroscopy (MRS). RESULTS: All participants demonstrated atrophy in at least one lobar structure or increased lateral ventricular volume. The globus pallidi and cerebellar grey matter were most likely to demonstrate atrophy and asymmetry. Perfusion imaging revealed significant reductions of cerebral blood flow in both occipital and right frontoparietal regions. Diffusion abnormalities were relatively less common though a subset analysis of participants with higher resolution DTI demonstrated additional abnormalities. All participants showed abnormal levels on at least one brain metabolite, most commonly in choline and N-acetylaspartate. CONCLUSION: We demonstrate the presence of coup-contrecoup perfusion injury patterns, widespread atrophy, regional brain volume asymmetry, and metabolic aberrations as sensitive markers of chronic mTBI sequelae. Our findings expand the historic focus on quantitative imaging of mTBI with DTI by highlighting the complementary importance of volumetry, arterial spin labeling perfusion and magnetic resonance spectroscopy neurometabolite analyses in the evaluation of chronic mTBI.

Association between neuroimaging and clinical outcomes in individuals with central nervous system cryptococcosis.

BACKGROUND: The radiological manifestations of central nervous system (CNS) cryptococcosis are diverse and often subtle. There is heterogeneity on how different neuroimaging patterns impact prognosis. This study aims to assess the association between the neuroimaging and clinical outcomes of CNS cryptococcosis. METHODS: All patients with CNS cryptococcosis between July 2017 and April 2023 who underwent brain magnetic resonance imaging (MRI) were included. The primary outcome was mortality during hospitalisation. Secondary outcomes were readmission, ventricular shunting, duration of hospitalisation and time to the first negative cerebrospinal fluid culture. We compared the outcomes for each of the five main radiological findings on the brain MRI scan. RESULTS: We included 46 proven CNS cryptococcosis cases. The two main comorbidity groups were HIV infection (20, 43%) and solid organ transplantation (10, 22%), respectively. Thirty-nine patients exhibited at least one radiological abnormality (85%), with the most common being meningeal enhancement (34, 74%). The mortality rates occurred at 11% (5/46) during hospitalisation. We found no significant disparities in mortality related to distinct radiological patterns. The presence of pseudocysts was significantly associated with the need for readmission (p = .027). The ventricular shunting was significantly associated with the presence of pseudocysts (p = .005) and hydrocephalus (p = .044). CONCLUSION: In this study, there is no association between brain MRI findings and mortality. Larger studies are needed to evaluate this important issue.

Genetic evidence supports a causal relationship between air pollution and brain imaging-derived phenotypes.

BACKGROUND: Observational studies have reported associations between air pollutants and brain imaging-derived phenotypes (IDPs); however, whether this relationship is causal remains uncertain. METHODS: We conducted bidirectional two-sample Mendelian randomization (MR) analyses to explore the causal relationships between 5 types of air pollutants (N=423,796 to 456,380 individuals) and 587 reliable IDPs (N=33,224 individuals). Two-step MR was also conducted to assess whether the identified effects are mediated through the modulation of circulating cytokines (N=8293). RESULTS: We found genetic evidence supporting the association of nitrogen oxides (NOx) with mean intra-cellular volume fraction (ICVF) in the left uncinate fasciculus (IVW ß=-0.42, 95 % CI -0.62 to -0.23, P=1.51×10-5) and mean fractional anisotropy (FA) in the left uncinate fasciculus (IVW ß=-0.42, 95 % CI -0.62 to -0.21, P=4.89×10-5). In further two-step MR analyses, we did not find evidence that genetic predictions of any circulating cytokines mediated the association between NOx and IDPs. CONCLUSION: This study provides evidence for the association between air pollutants and brain IDPs, emphasizing the importance of controlling air pollution to improve brain health.

Investigating grey matter volumetric trajectories through the lifespan at the individual level.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Neuroimaging considerations in abusive head trauma.

This focused review on abusive head trauma describes the injuries to the head, brain and/or spine of an infant or young child from inflicted trauma and their neuroimaging correlates. Accurate recognition and diagnosis of abusive head trauma is paramount to prevent repeated injury, provide timely treatment, and ensure that accidental or underlying medical contributors have been considered. In this article, we aim to discuss the various findings on neuroimaging that have been associated with AHT, compared to those that are more consistent with accidental injuries or with underlying medical causes that may also be on the differential.