Guardians of memory: The urgency of early dementia screening in an aging society.

The global aging population has led to a significant rise in the prevalence of age-related non-communicable diseases such as dementia and other cognitive disorders. In 2019, there were 57.4 million people with dementia worldwide, and this number is projected to triple by 2050. Intervening in and managing 12 potentially modifiable dementia risk factors can prevent or delay the onset and progression of about 40% of dementia cases. Neuroimaging, biomarkers, and advanced neuropsychological testing offer promising pathways for the early detection of dementia. Emphasis should be placed on educating the public about the importance of brain health and the early signs of cognitive impairment, as well as promoting dementia prevention measures. Adopting a healthy lifestyle - including a balanced diet, regular physical exercise, active social engagement, cognitive activities, and avoiding smoking and excessive alcohol consumption - can help reduce the risk of cognitive decline and prevent cognitive disorders. Government policies on dementia prevention and health care, along with early and regular dementia screening programs, can enhance the early identification and management of individuals at risk. In addition, integrating cognitive health assessments into routine medical check-ups is essential for the early screening and management of dementia.

Correlation analysis and predictive model construction of metabolic syndrome, complete blood count-derived inflammatory markers, and overall burden of cerebral small vessel disease.

Background: The high burden of cerebral small vessel disease (CSVD) on neuroimaging is a significant risk factor for stroke, cognitive dysfunction, and emotional disorders. Currently, there is a lack of studies investigating the correlation between metabolic syndrome (MetS), complete blood count-derived inflammatory markers, and total CSVD burden. This study aims to evaluate the total CSVD imaging load using machine learning (ML) algorithms and to explore further the relationship between MetS, complete blood count-derived inflammatory markers, and CSVD load. Methods: We included CSVD patients from Xijing Hospital (2012-2022). Univariate and lasso regression analyses identified variables linked to CSVD neuroimaging burden. Six ML models predicted CSVD burden based on MetS and inflammatory markers. Model performance was evaluated using ROCauc, PRauc, DCA, and calibration curves. The SHAP method validated model interpretability. The best-performing model was selected to develop a web-based calculator using the Shiny package. Results: The Logistic regression model outperformed others in predicting CSVD burden. The model incorporated MetS, neutrophil-to-lymphocyte ratio (NLR), homocysteine (Hcy), age, smoking status, cystatin C (CysC), uric acid (UA), and prognostic nutritional index (PNI). Conclusion: MetS, NLR, Hcy and CSVD high load were positively correlated, and the Logistic regression model could accurately predict the total CSVD load degree.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research.

INTRODUCTION: We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4). METHODS: Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. RESULTS: Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. DISCUSSION: Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. HIGHLIGHTS: A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

Progress towards understanding risk factor mechanisms in the development of autism spectrum disorders.

Autism spectrum disorders (ASD) are a heterogenous set of syndromes characterised by social impairment and cognitive symptoms. Currently, there are limited treatment options available to help people with ASD manage their symptoms. Understanding the biological mechanisms that result in ASD diagnosis and symptomatology is an essential step in developing new interventional strategies. Human genetic studies have identified common gene variants of small effect and rare risk genes and copy number variants (CNVs) that substantially increase the risk of developing ASD. Reverse translational studies using rodent models based on these genetic variants provide new insight into the biological basis of ASD. Here we review recent findings from three ASD associated CNV mouse models (16p11.2, 2p16.3 and 22q11.2 deletion) that show behavioural and cognitive phenotypes relevant to ASD. These models have identified disturbed excitation-inhibition neurotransmitter balance, evidenced by dysfunctional glutamate and GABA signalling, as a key aetiological mechanism. These models also provide emerging evidence for serotoninergic neurotransmitter system dysfunction, although more work is needed to clarify the nature of this. At the brain network level, prefrontal cortex (PFC) dysfunctional connectivity is also evident across these models, supporting disturbed PFC function as a key nexus in ASD aetiology. Overall, published data highlight the utility and valuable insight gained into ASD aetiology from preclinical CNV mouse models. These have identified key aetiological mechanisms that represent putative novel therapeutic targets for the treatment of ASD symptoms, making them useful translational models for future drug discovery, development and validation.

Biomarker-Guided Tailored Therapy in Major Depression.

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

The neurobehavioural effects of cannabidiol in alcohol use disorder: Study protocol for a double-blind, randomised, cross over, placebo-controlled trial.

Current treatments for alcohol use disorders (AUD) have limited efficacy. Recently, Cannabidiol (CBD) has been examined in a multitude of clinical settings. Preclinical and clinical results suggest that CBD might be particularly well suited for the treatment of AUD and may reduce alcohol cue and stress-induced craving and alcohol seeking. This study aims to investigate this new pharmacotherapy with a particular focus on neurobiological and physiological indicators of craving. Methods: In this double-blind, within-subject, randomised, placebo-controlled, cross-over study, non-treatment seekers will be randomly allocated to three days of four 200 mg CBD gel capsules (800 mg/day) or placebo, with an 18-day washout period. Cognitive, clinical, and neuroimaging assessments will be completed during these three days. The CBD and placebo assessments will be compared. The primary outcomes are i) BOLD signal as a proxy for regional activity during a cue reactivity and a fear response task measured with functional magnetic resonance imaging (fMRI), ii) heart rate variability and skin conductance levels as a proxy for psychophysiological responses to alcohol stimuli. The secondary outcomes are: i) neurometabolite levels (γ-Aminobutyric acid, ethanol, glutathione, and glutamate + glutamine (combined signal)) using magnetic resonance spectroscopy (MRS); ii) functional connectivity using resting state fMRI (rsfMRI); iii) executive functioning task results; iv) clinical outcomes such as craving, anxiety, and sleep. Discussion: This study will improve the understanding of the mechanisms of action of CBD and provide early signals of efficacy regarding the therapeutic potential of CBD in the treatment of alcohol use disorder. ClinicalTrials.gov Identifier: NCT05387148.

Not All Stroop-Type Tasks Are Alike: Assessing the Impact of Stimulus Material, Task Design, and Cognitive Demand via Meta-analyses Across Neuroimaging Studies.

The Stroop effect is one of the most often studied examples of cognitive conflict processing. Over time, many variants of the classic Stroop task were used, including versions with different stimulus material, control conditions, presentation design, and combinations with additional cognitive demands. The neural and behavioral impact of this experimental variety, however, has never been systematically assessed. We used activation likelihood meta-analysis to summarize neuroimaging findings with Stroop-type tasks and to investigate whether involvement of the multiple-demand network (anterior insula, lateral frontal cortex, intraparietal sulcus, superior/inferior parietal lobules, midcingulate cortex, and pre-supplementary motor area) can be attributed to resolving some higher-order conflict that all of the tasks have in common, or if aspects that vary between task versions lead to specialization within this network. Across 133 neuroimaging experiments, incongruence processing in the color-word Stroop variant consistently recruited regions of the multiple-demand network, with modulation of spatial convergence by task variants. In addition, the neural patterns related to solving Stroop-like interference differed between versions of the task that use different stimulus material, with the only overlap between color-word, emotional picture-word, and other types of stimulus material in the posterior medial frontal cortex and right anterior insula. Follow-up analyses on behavior reported in these studies (in total 164 effect sizes) revealed only little impact of task variations on the mean effect size of reaction time. These results suggest qualitative processing differences among the family of Stroop variants, despite similar task difficulty levels, and should carefully be considered when planning or interpreting Stroop-type neuroimaging experiments.

Behavioral disorders in multiple sclerosis: a comprehensive review.

Multiple sclerosis (MS) is a heterogenous autoimmune-mediated disease of the central nervous system (CNS) characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of neuropsychiatric symptoms, behavioral changes are common, even from the early stages of the disease, while they are associated with cognitive deficits in advanced MS. According to DSM-5, behavioral disorders include attention deficits, oppositional, defiant and conduct disorders, anxiety, panic, obsessive-compulsive disorders (OCD), disruptive and emotional disorders, while others include also irritability, agitation, aggression and executive dysfunctions. Approximately 30 to 80% of individuals with MS demonstrate behavioral changes associated with disease progression. They are often combined with depression and other neuropsychiatric disorders, but usually not correlated with motor deficits, suggesting different pathomechanisms. These and other alterations contribute to disability in MS. While no specific neuropathological data for behavioral changes in MS are available, those in demyelination animal models share similarities with white matter and neuroinflammatory abnormalities in humans. Neuroimaging revealed prefrontal cortical atrophy, interhemispheric inhibition and disruption of fronto-striato-thalamic and frontoparietal networks. This indicates multi-regional patterns of cerebral disturbances within the MS pathology although their pathogenic mechanisms await further elucidation. Benefits of social, psychological, behavioral interventions and exercise were reported. Based on systematical analysis of PubMed, Google Scholar and Cochrane library, current epidemiological, clinical, neuroimaging and pathogenetic evidence are reviewed that may aid early identification of behavioral symptoms in MS, and promote new therapeutic targets and strategies.

MADE-for-ASD: A multi-atlas deep ensemble network for diagnosing Autism Spectrum Disorder.

In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset - both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as precuneus and anterior cingulate/ventromedial. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at https://github.com/hasan-rakibul/MADE-for-ASD.

Storyteller in ADNI4: Application of an early Alzheimer's disease screening tool using brief, remote, and speech-based testing.

INTRODUCTION: Speech-based testing shows promise for sensitive and scalable objective screening for Alzheimer's disease (AD), but research to date offers limited evidence of generalizability. METHODS: Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) studies (N = 101, N = 46 mild cognitive impairment [MCI]) and Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4) remote digital (N = 426, N = 58 self-reported MCI, mild AD or dementia) and in-clinic (N = 57, N = 13 MCI) cohorts, in which participants provided audio-recorded responses to automated remote story recall tasks in the Storyteller test battery. Text similarity, lexical, temporal, and acoustic speech feature sets were extracted. Models predicting early AD were developed in AMYPRED and tested out of sample in the demographically more diverse cohorts in ADNI4 (> 33% from historically underrepresented populations). RESULTS: Speech models generalized well to unseen data in ADNI4 remote and in-clinic cohorts. The best-performing models evaluated text-based metrics (text similarity, lexical features: area under the curve 0.71-0.84 across cohorts). DISCUSSION: Speech-based predictions of early AD from Storyteller generalize across diverse samples. HIGHLIGHTS: The Storyteller speech-based test is an objective digital prescreener for Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4). Speech-based models predictive of Alzheimer's disease (AD) were developed in the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) sample (N = 101). Models were tested out of sample in ADNI4 in-clinic (N = 57) and remote (N = 426) cohorts. Models showed good generalization out of sample. Models evaluating text matching and lexical features were most predictive of early AD.

Glymphatic influx and clearance are perturbed in Huntington's disease.

The accumulation of mutant huntingtin protein aggregates in neurons is a pathological hallmark of Huntington's disease (HD). The glymphatic system, a brain-wide perivascular network, facilitates the exchange of interstitial fluid (ISF) and cerebrospinal fluid (CSF), supporting interstitial solute clearance of brain wastes. In this study, we employed dynamic glucose-enhanced (DGE) MRI to measure D-glucose clearance from CSF as a tool to predict glymphatic function in a mouse model of HD. We found significantly diminished CSF clearance efficiency in HD mice prior to phenotypic onset. The impairment of CSF clearance efficiency worsened with disease progression. These DGE MRI findings in compromised glymphatic function were further confirmed with fluorescence-based imaging of CSF tracer influx, suggesting an impaired glymphatic function in premanifest HD. Moreover, expression of the astroglial water channel aquaporin-4 (AQP4) in the perivascular compartment, a key mediator of glymphatic function, was significantly diminished in both HD mouse brain and human HD brain. Our data, acquired using a clinically translatable MRI, indicate a perturbed glymphatic network in the HD brain. Further validation of these findings in clinical studies will provide insights into the potential of glymphatic clearance as a therapeutic target as well as an early biomarker in HD.

The Promise and Challenges of Integrating Biological and Prevention Sciences: A Community-Engaged Model for the Next Generation of Translational Research.

Beginning with the successful sequencing of the human genome two decades ago, the possibility of developing personalized health interventions based on one's biology has captured the imagination of researchers, medical providers, and individuals seeking health care services. However, the application of a personalized medicine approach to emotional and behavioral health has lagged behind the development of personalized approaches for physical health conditions. There is potential value in developing improved methods for integrating biological science with prevention science to identify risk and protective mechanisms that have biological underpinnings, and then applying that knowledge to inform prevention and intervention services for emotional and behavioral health. This report represents the work of a task force appointed by the Board of the Society for Prevention Research to explore challenges and recommendations for the integration of biological and prevention sciences. We present the state of the science and barriers to progress in integrating the two approaches, followed by recommended strategies that would promote the responsible integration of biological and prevention sciences. Recommendations are grounded in Community-Based Participatory Research approaches, with the goal of centering equity in future research aimed at integrating the two disciplines to ultimately improve the well-being of those who have disproportionately experienced or are at risk for experiencing emotional and behavioral problems.

Neuroimaging evidence of disturbed self-appraisal in posttraumatic stress disorder: A systematic review.

BACKGROUND: The experience of self-hood in posttraumatic stress disorder (PTSD) is altered cognitively and somatically. Dysfunctional negative cognitions about the self are a central mechanism of PTSD symptomatology and treatment. However, while higher-order brain models of disturbances in self-appraisal (i.e., cognitive processes relating to evaluating the self) have been examined in other psychiatric disorders, it is unclear how normative brain function during self-appraisal is impaired in PTSD. METHODS: This paper presents a PRISMA systematic review of functional neuroimaging studies (n = 5), to establish a neurobiological account of how self-appraisal processes are disturbed in PTSD. The review was prospectively registered with PROSPERO (CRD42023450509). RESULTS: Self-appraisal in PTSD is linked to disrupted activity in core self-processing regions of the Default Mode Network (DMN); and regions involved in cognitive control and emotion regulation, salience and valuation. LIMITATIONS: Because self-appraisal in PTSD is relatively under-studied, only a small number of studies could be included for review. Cross-study heterogeneity in analytic approaches and trauma-exposure history prohibited a quantitative meta-analysis. CONCLUSIONS: This paper proposes a mechanistic account of how neural dysfunctions may manifest clinically in PTSD and inform targeted selection of appropriate treatment options. We present a research agenda for future work to advance the field.

Behavioral disorders in dementia with Lewy bodies: old and new knowledge.

Dementia with Lewy bodies (DLB), the second most common primary degenerative neurocognitive disorder after Alzheimer disease, is frequently preceded by REM sleep behavior disorders (RBD) and other behavioral symptoms, like anxiety, irritability, agitation or apathy, as well as visual hallucinations and delusions, most of which occurring in 40-60% of DLB patients. Other frequent behavioral symptoms like attention deficits contribute to cognitive impairment, while attention-deficit/hyperactivity disorder (ADHD) is a risk factor for DLB. Behavioral problems in DLB are more frequent, more severe and appear earlier than in other neurodegenerative diseases and, together with other neuropsychiatric symptoms, contribute to impairment of quality of life of the patients, but their pathophysiology is poorly understood. Neuroimaging studies displayed deficits in cholinergic brainstem nuclei and decreased metabolism in frontal, superior parietal regions, cingulate gyrus and amygdala in DLB. Early RBD in autopsy-confirmed DLB is associated with lower Braak neuritic stages, whereas those without RBD has greater atrophy of hippocampus and increased tau burden. αSyn pathology in the amygdala, a central region in the fear circuitry, may contribute to the high prevalence of anxiety, while in attention dysfunctions the default mode and dorsal attention networks displayed diverging activity. These changes suggest that behavioral disorders in DLB are associated with marked impairment in large-scale brain structures and functional connectivity network disruptions. However, many pathobiological mechanisms involved in the development of behavioral disorders in DLB await further elucidation in order to allow an early diagnosis and adequate treatment to prevent progression of these debilitating disorders.

Migraine and brain structure in the elderly: The Rotterdam Study.

BACKGROUND: Recent studies suggested that persons with migraine might be at higher risk of structural brain changes, including cerebral small vessel disease and atrophy. However, findings in the literature are inconsistent, with variations observed in the direction, magnitude, and population characteristics of reported effects, and large-scale population-based evidence remains scarce. Hence, we investigated the association of migraine with structural brain changes in a middle-aged and elderly population. METHODS: Within the population-based Rotterdam Study, lifetime history of migraine was assessed using a validated questionnaire between 2006 and 2011. Magnetic resonance imaging of the brain was performed in 4920 participants (median age 61.7 [IQR 45.5, 97.5] years, 55.4% female) to assess imaging markers of cerebral small vessel disease and brain atrophy. We used linear and logistic regression models to examine the cross-sectional association of migraine with brain volumes (total grey and white matter volumes in mL) and cerebral small vessel disease markers (white matter hyperintensity volume in mL, presence of lacunes and cerebral microbleeds). Adjustments were made for age, sex, intracranial volume and cardiovascular variables. Analyses were also stratified by sex and presence of aura. RESULTS: The lifetime prevalence of migraine was 15.3% (752/4920). In multivariable adjusted regression models, we found no statistically significant differences between participants with and without migraine in terms of total brain volume (mean difference [MD]: 2.21 mL, 95% confidence interval [CI]: -0.38 ; 4.81), grey matter volume (MD: 0.38 mL, 95% CI: -1.98 ; 2.74), white matter volume (MD: 2.19 mL, 95% CI: -0.56 ; 4.93), log white matter hyperintensity volume (MD: -0.04 mL, 95% CI: -0.10 ; 0.02), presence of lacunes (odds ratio [OR]: 0.82, 95% CI: 0.58-1.15), and presence of cerebral microbleeds (OR: 0.95, 95% CI: 0.76-1.18). CONCLUSION: In this study, we found that middle-aged and elderly participants with migraine were not more likely to have structural brain changes on magnetic resonance imaging.

The role of the Alzheimer's Association in the genesis of Alzheimer's Disease Neuroimaging Initiative.

Here we highlight the Alzheimer's Association's role since its inception, as a strategic collaborator with National Institutes of Health-National Institute on Aging in the development of the modern era of the Alzheimer's Movement and in making Alzheimer's disease (AD) a national priority in the United States by developing several initiatives to advance knowledge about the cause, diagnosis, and treatment of dementia. Among these collaborative undertakings, the Alzheimer's Disease Neuroimaging Initiative (ADNI) is an exemplary case, launched with groundwork by the Neuroimaging Working Group sponsored by the Association's Ronald and Nancy Reagan Research Institute on AD. The unique contribution of the Association to the development of ADNI includes participation as a member of ADNI's Private Partner Scientific Board and involvement in developing an AD biomarker standardization and validation subproject, which has led to a conceptual shift in the field to define AD based on its underlying biology. Furthermore, the creation of Worldwide ADNI (WW-ADNI) is highlighted, underscoring the global impact of these efforts. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a keystone undertaking in the evolving landscape of Alzheimer's disease (AD) research, and is now in its fourth iteration. The Alzheimer's Association has partnered with ADNI since its inception. ADNI 4 and the Association continue to collaborate, ensuring representation within the study population.

Biomarkers for Psychosis: Are We There Yet? Umbrella Review of 1478 Biomarkers.

Background and Hypothesis: This umbrella review aims to comprehensively synthesize the evidence of association between peripheral, electrophysiological, neuroimaging, neuropathological, and other biomarkers and diagnosis of psychotic disorders. Study Design: We selected systematic reviews and meta-analyses of observational studies on diagnostic biomarkers for psychotic disorders, published until February 1, 2018. Data extraction was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Evidence of association between biomarkers and psychotic disorders was classified as convincing, highly suggestive, suggestive, weak, or non-significant, using a standardized classification. Quality analyses used the Assessment of Multiple Systematic Reviews (AMSTAR) tool. Study Results: The umbrella review included 110 meta-analyses or systematic reviews corresponding to 3892 individual studies, 1478 biomarkers, and 392 210 participants. No factor showed a convincing level of evidence. Highly suggestive evidence was observed for transglutaminase autoantibodies levels (odds ratio [OR] = 7.32; 95% CI: 3.36, 15.94), mismatch negativity in auditory event-related potentials (standardized mean difference [SMD] = 0.73; 95% CI: 0.5, 0.96), P300 component latency (SMD = -0.6; 95% CI: -0.83, -0.38), ventricle-brain ratio (SMD = 0.61; 95% CI: 0.5, 0.71), and minor physical anomalies (SMD = 0.99; 95% CI: 0.64, 1.34). Suggestive evidence was observed for folate, malondialdehyde, brain-derived neurotrophic factor, homocysteine, P50 sensory gating (P50 S2/S1 ratio), frontal N-acetyl-aspartate, and high-frequency heart rate variability. Among the remaining biomarkers, weak evidence was found for 626 and a non-significant association for 833 factors. Conclusions: While several biomarkers present highly suggestive or suggestive evidence of association with psychotic disorders, methodological biases, and underpowered studies call for future higher-quality research.

Subcortical volume in middle-aged adults with fetal alcohol spectrum disorders.

Studies of youth and young adults with prenatal alcohol exposure (PAE) have most consistently reported reduced volumes of the corpus callosum, cerebellum and subcortical structures. However, it is unknown whether this continues into middle adulthood or if individuals with PAE may experience premature volumetric decline with aging. Forty-eight individuals with fetal alcohol spectrum disorders (FASD) and 28 healthy comparison participants aged 30 to 65 participated in a 3T MRI session that resulted in usable T1-weighted and T2-weighted structural images. Primary analyses included volumetric measurements of the caudate, putamen, pallidum, cerebellum and corpus callosum using FreeSurfer software. Analyses were conducted examining both raw volumetric measurements and subcortical volumes adjusted for overall intracranial volume (ICV). Models tested for main effects of age, sex and group, as well as interactions of group with age and group with sex. We found the main effects for group; all regions were significantly smaller in participants with FASD for models using raw volumes (P's < 0.001) as well as for models using volumes adjusted for ICV (P's < 0.046). Although there were no significant interactions of group with age, females with FASD had smaller corpus callosum volumes relative to both healthy comparison females and males with FASD (P's < 0.001). As seen in children and adolescents, adults aged 30 to 65 with FASD showed reduced volumes of subcortical structures relative to healthy comparison adults, suggesting persistent impact of PAE. Moreover, the observed volumetric reduction of the corpus callosum in females with FASD could suggest more rapid degeneration, which may have implications for cognition as these individuals continue to age.

Atlas-based assessment of hypomyelination: Quantitative MRI in Pelizaeus-Merzbacher disease.

Pelizaeus-Merzbacher disease (PMD) is a rare childhood hypomyelinating leukodystrophy. Quantification of the pronounced myelin deficit and delineation of subtle myelination processes are of high clinical interest. Quantitative magnetic resonance imaging (qMRI) techniques can provide in vivo insights into myelination status, its spatial distribution, and dynamics during brain maturation. They may serve as potential biomarkers to assess the efficacy of myelin-modulating therapies. However, registration techniques for image quantification and statistical comparison of affected pediatric brains, especially those of low or deviant image tissue contrast, with healthy controls are not yet established. This study aimed first to develop and compare postprocessing pipelines for atlas-based quantification of qMRI data in pediatric patients with PMD and evaluate their registration accuracy. Second, to apply an optimized pipeline to investigate spatial myelin deficiency using myelin water imaging (MWI) data from patients with PMD and healthy controls. This retrospective single-center study included five patients with PMD (mean age, 6 years ± 3.8) who underwent conventional brain MRI and diffusion tensor imaging (DTI), with MWI data available for a subset of patients. Three methods of registering PMD images to a pediatric template were investigated. These were based on (a) T1-weighted (T1w) images, (b) fractional anisotropy (FA) maps, and (c) a combination of T1w, T2-weighted, and FA images in a multimodal approach. Registration accuracy was determined by visual inspection and calculated using the structural similarity index method (SSIM). SSIM values for the registration approaches were compared using a t test. Myelin water fraction (MWF) was quantified from MWI data as an assessment of relative myelination. Mean MWF was obtained from two PMDs (mean age, 3.1 years ± 0.3) within four major white matter (WM) pathways of a pediatric atlas and compared to seven healthy controls (mean age, 3 years ± 0.2) using a Mann-Whitney U test. Our results show that visual registration accuracy estimation and computed SSIM were highest for FA-based registration, followed by multimodal, and T1w-based registration (SSIMFA = 0.67 ± 0.04 vs. SSIMmultimodal = 0.60 ± 0.03 vs. SSIMT1 = 0.40 ± 0.14). Mean MWF of patients with PMD within the WM pathways was significantly lower than in healthy controls MWFPMD = 0.0267 ± 0.021 vs. MWFcontrols = 0.1299 ± 0.039. Specifically, MWF was measurable in brain structures known to be myelinated at birth (brainstem) or postnatally (projection fibers) but was scarcely detectable in other brain regions (commissural and association fibers). Taken together, our results indicate that registration accuracy was highest with an FA-based registration pipeline, providing an alternative to conventional T1w-based registration approaches in the case of hypomyelinating leukodystrophies missing normative intrinsic tissue contrasts. The applied atlas-based analysis of MWF data revealed that the extent of spatial myelin deficiency in patients with PMD was most pronounced in commissural and association and to a lesser degree in brainstem and projection pathways.

A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use.

Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a predictor of substance use or a marker of the inclination to engage in such behavior. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1000 participants. Behaviors and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use.