Structural Neuroimaging Research Articles

Investigating Compensatory Brain Activity in Older Adults with Subjective Cognitive Decline.

Preclinical Alzheimer's disease (AD) is one possible cause of subjective cognitive decline (SCD). Normal task performance despite ongoing neurodegeneration is typically considered as neuronal compensation, which is reflected by greater neuronal activity. Compensatory brain activity has been observed in frontal as well as parietal regions in SCD, but data are scarce, especially outside the memory domain. To investigate potential compensatory activity in SCD. Such compensatory activity is particularly expected in participants where blood-based biomarkers indicated amyloid positivity as this implies preclinical AD. 52 participants with SCD (mean age: 71.00±5.70) underwent structural and functional neuroimaging (fMRI), targeting episodic memory and spatial abilities, and a neuropsychological assessment. The estimation of amyloid positivity was based on plasma amyloid-β and phosphorylated tau (pTau181) measures. Our fMRI analyses of the spatial abilities task did not indicate compensation, with only three voxels exceeding an uncorrected threshold at p < 0.001. This finding was not replicated in a subset of 23 biomarker positive individuals. Our results do not provide conclusive evidence for compensatory brain activity in SCD. It is possible that neuronal compensation does not manifest at such an early stage as SCD. Alternatively, it is possible that our sample size was too small or that compensatory activity may be too heterogeneous to be detected by group-level statistics. Interventions based on the individual fMRI signal should therefore be explored.

Reduced memory precision in older age is associated with functional and structural differences in the angular gyrus

Decreased fidelity of mnemonic representations plays a critical role in age-related episodic memory deficits, yet the brain mechanisms underlying such reductions remain unclear. Using functional and structural neuroimaging, we examined how changes in two key nodes of the posterior-medial network, the hippocampus and the angular gyrus (AG), might underpin loss of memory precision in older age. Healthy young and older adults completed a memory task that involved reconstructing object features on a continuous scale. Investigation of blood-oxygen-level-dependent (BOLD) activity during retrieval revealed an age-related reduction in activity reflecting successful recovery of object features in the hippocampus, whereas trial-wise modulation of BOLD signal by graded memory precision was diminished in the AG. Gray matter volume of the AG further predicted individual differences in memory precision in older age, beyond likelihood of successful retrieval. These findings provide converging evidence for a role of functional and structural integrity of the AG in constraining the fidelity of episodic remembering in older age, yielding new insights into parietal contributions to age-related episodic memory decline.

Patterns of risk-Using machine learning and structural neuroimaging to identify pedophilic offenders.

Child sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA. To use machine learning and MRI data to identify PO individuals. From a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals. The classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P 5000 = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending. Aberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts.

Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer's disease and neurodegeneration stratified by sex.

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer's disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD, and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging in non-demented individuals with abnormal levels of biomarkers of AD and axonal injury.

Comparing survival and mortality in patients with late-onset and young-onset vascular dementia.

Vascular dementia (VD) is one of the more common types of dementia. Much is known about VD in older adults in terms of survival and associated risk factors, but comparatively less is known about VD in a younger population. This study aimed to investigate survival in people with young-onset VD (YO-VD) compared to those with late-onset VD (LO-VD) and to investigate predictors of mortality. Retrospective file review from 1992 to 2014. The inpatient unit of a tertiary neuropsychiatry service in Victoria, Australia. Inpatients with a diagnosis of VD. Mortality information was obtained from the Australian Institute of Health and Welfare. Clinical variables included age of onset, sex, vascular risk factors, structural neuroimaging, and Hachinksi scores. Statistical analyses used were Kaplan-Meier curves for median survival and Cox regression for predictors of mortality. Eighty-four participants were included with few clinical differences between the LO-VD and YO-VD groups. Sixty-eight (81%) had died. Median survival was 9.9 years (95% confidence interval 7.9, 11.7), with those with LO-VD having significantly shorter survival compared to those with YO-VD (6.1 years and 12.8 years, respectively) and proportionally more with LO-VD had died (94.6%) compared to those with YO-VD (67.5%), χ2(1) = 9.16, p = 0.002. The only significant predictor of mortality was increasing age (p = 0.001). While there were few clinical differences, and older age was the only factor associated with survival, further research into the effects of managing cardiovascular risk factors and their impact on survival are recommended.

Brain Mapping in Neurosurgery

Background and Aim: Brain mapping is the study of the anatomy and function of the Central Nervous System (CNS). Brain mapping has many techniques and these techniques are permanently changing and updating. From the beginning, brain mapping was invasive and for brain mapping, electrical stimulation of the exposed brain was needed. However, nowadays brain mapping does not require electrical stimulation and often does not require any complex involvement of patients. To perform brain mapping, functional and structural neuroimaging has an essential role. The techniques for brain mapping include noninvasive techniques (structural and functional magnetic resonance imaging [fMRI], diffusion MRI [dMRI], magnetoencephalography [MEG], electroencephalography [EEG], positron emission tomography [PET], near-infrared spectroscopy [NIRS] and other non-invasive scanning techniques) and invasive techniques (direct cortical stimulation [DCS] and intracarotid amytal test [IAT] or wada test). Methods and Materials/Patients: This is a narrative study on brain mapping in neurosurgery. To provide up-to-date information on brain mapping in neurosurgery, we precisely reviewed brain mapping and neurosurgery articles. Using the keywords “brain mapping”, “neurosurgery”, “brain mapping techniques”, and “benefits of brain mapping”, all of the related articles were obtained from Google Scholar, PubMed, and Medline and were precisely studied. Results: To perform an effective and safe neurosurgical intervention, precise information about the structural and functional anatomy of the brain is obligatory. Based on the information on brain mapping, the selection of suitable patients for the operation, the plan of appropriate operative approach, and good surgical results can be acquired. To provide this information, we can use brain mapping techniques that were formerly applied in neuroscientific brain mapping efforts with noninvasive techniques, such as fMRI, MEG, dMRI, PET, etc and invasive techniques, such as DCS, IAT, etc. Conclusion: Functional brain mapping is a constantly evolving fact in neurosurgery. All stages in obtaining a functional image are complex and need knowledge of the basic physiologic and imaging features.

Functional septorhinoplasty alters brain structure and function: Neuroanatomical correlates of olfactory dysfunction.

We previously demonstrated functionally significant structural plasticity within the central olfactory networks, in association with improved olfaction after surgical treatment of chronic rhinosinusitis (CRS). In order to confirm and expand on these findings, the primary aim of this study was to determine whether these same regions undergo functionally significant structural plasticity following functional septorhinoplasty (fSRP), in patients with non-CRS olfactory dysfunction (OD) of mixed cause. fSRP has previously been shown to improve olfactory function, and the secondary aim of this study was to provide initial insights into the mechanism by which fSRP affects olfaction. We performed a pilot prospective, multimodal neuroimaging study in 20 participants undergoing fSRP, including patients with non-CRS OD of mixed cause, as well as normosmic surgical controls. Participants underwent psychophysical olfactory testing, assessment of nasal airway, structural and functional neuroimaging. This was performed pre- and postoperatively in patients, and preoperatively in controls. There was a statistically and clinically significant improvement in mean psychophysical olfactory scores after surgery. This was associated with structural and functional plasticity within areas of the central olfactory network (anterior cingulate, orbitofrontal cortex, insula, temporal pole). Improved psychophysical scores were significantly correlated with change in bilateral measures of nasal airflow, not measures of airflow symmetry, suggesting that improved overall airflow was more important than correction of septal deviation. This work highlights the importance of these neuroanatomical regions as potential structural correlates of olfactory function and dysfunction. Our results also provide initial insight into the mechanistic effects of fSRP on olfaction. Further work could investigate the utility of these regions as personalised biomarkers of OD, as well as the role of fSRP in treating OD.

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, &amp; Neuroimaging Advancing the Field of Neurology

Brain Connectivity: A Journal of Clinical Neurology, Neuroscience, &amp; Neuroimaging Advancing the Field of Neurology

325 Predictors of Substance Use Initiation by Late Childhood: Findings from the Adolescent Brain Cognitive Development (ABCD) Study

OBJECTIVES/GOALS: Adolescence represents a critical period for substance use initiation. Various factors may contribute to trying a sip or single puff of a substance, that could lead to more frequent use. However, less is known about how predictors from multiple domains converge to impact risk for general substance use initiation. METHODS/STUDY POPULATION: The Adolescent Brain Cognitive Development (ABCD) study is a multi-site longitudinal study following youth into early adulthood. The present study included 7,644 ABCD children who reported no lifetime substance use (including any experimentation) at baseline (ages 9–10). Our primary aim was to use a random forest classification model to predict binary substance use initiation, defined as trying any non-prescribed substance (e.g., alcohol, tobacco, cannabis, non-prescribed medications), during a 2-year follow-up after baseline. A total of 402 variables from the following categories were examined as predictors: demographics, peer substance use and availability, mental and physical health, culture and environment, biospecimens, neurocognitive functioning, and structural neuroimaging variables. RESULTS/ANTICIPATED RESULTS: Over a two-year follow-up, 751 (9.8%) of substance-naïve children reported trying a substance by age 11. The most common substance was alcohol, followed by cannabis and tobacco. Mean Decrease Accuracy (MDA) values were used to assess the relative importance of each predictor. The overall accuracy of the model in accurately predicting group membership (no substance use initiation vs. substance use initiation) was 57.66%. Of the top 5 predictors, the most important predictor was intent to use alcohol (MDA = .002). The following top predictors were structural neuroimaging variables: volume and surface area of right lateral occipital lobe (MDA = .0009 and .0008, respectively), surface area of right inferior temporal lobe (MDA = .0007), and surface area of left superior frontal lobe (MDA = .0007). DISCUSSION/SIGNIFICANCE: A combination of intent to use alcohol and structural neuroimaging indices were among the top predictors of substance use initiation. Understanding predictors of early substance use experimentation is important for identifying at-risk youth that may require targeted intervention approaches.

292 Hyperorality in Frontotemporal Dementia: Psychiatric and Neural Correlates Across the Disease Course

OBJECTIVES/GOALS: To describe cognitive and psychiatric symptom profiles of individuals with bvFTD and hyperorality. We test two hypotheses: (1) individuals with hyperorality show more severe psychiatric profiles and (2) neuroanatomic correlates of hyperorality in advanced bvFTD differ from those with early bvFTD. METHODS/STUDY POPULATION: Participants were enrolled in ALLFTD–a multi-site longitudinal study in FTD. We selected the 354 participants who had a primary clinical diagnosis of bvFTD, 344 of whom had data on hyperorality. Each participant underwent extensive clinical interviews and examinations, structural neuroimaging, and blood sampling. Five anatomic regions of interest were identified and analyzed based on previously identified neuroanatomic correlates of hyperorality. Differences in participant characteristics and clinical outcomes were compared using t-tests for continuous variables and Pearsonχ2 tests for categorical variables. Linear multivariate regression controlling for age and total intracranial volume (TIV) was used to examine associations between atrophy in regions of interest and hyperorality status. RESULTS/ANTICIPATED RESULTS: Early-stage participants with hyperorality had poorer self-monitoring, empathic concern, and perspective taking as well as higher CDR behavioral subscale scores compared to those without hyperorality. Advanced stage participants with hyperorality had higher scores on the Social Behavior Observer Checklist compared to those without hyperorality. Early-stage participants with hyperorality displayed higher rates of ritualistic/compulsive behavior and motor disturbance. Advanced stage participants had higher rates of apathy, ritualistic/compulsive behavior, anxiety, and elation. In the advanced stage participants, hyperorality was associated with atrophy in the right dorsal striatum, the right ventral striatum, and the right insula cortex. DISCUSSION/SIGNIFICANCE: Hyperorality emerges early and is accompanied by neuropsychiatric symptoms prior to significant neurodegeneration. Overtime, participants with hyperorality develop more psychiatric symptoms as well as atrophy in striatal and insular brain regions. Our findings suggest a role for novel interventions like non-invasive brain stimulation.

Structural Neuroimaging in Adults and Adolescents With Newly Diagnosed Focal Epilepsy: The Human Epilepsy Project.

Structural Neuroimaging in Adults and Adolescents With Newly Diagnosed Focal Epilepsy: The Human Epilepsy Project.

Epigenetic aging and PTSD outcomes in the immediate aftermath of trauma.

Psychological trauma exposure and posttraumatic stress disorder (PTSD) have been associated with advanced epigenetic age. However, whether epigenetic aging measured at the time of trauma predicts the subsequent development of PTSD outcomes is unknown. Moreover, the neural substrates underlying posttraumatic outcomes associated with epigenetic aging are unclear. We examined a multi-ancestry cohort of women and men (n = 289) who presented to the emergency department (ED) after trauma. Blood DNA was collected at ED presentation, and EPIC DNA methylation arrays were used to assess four widely used metrics of epigenetic aging (HorvathAge, HannumAge, PhenoAge, and GrimAge). PTSD symptoms were evaluated longitudinally at the time of ED presentation and over the ensuing 6 months. Structural and functional neuroimaging was performed 2 weeks after trauma. After covariate adjustment and correction for multiple comparisons, advanced ED GrimAge predicted increased risk for 6-month probable PTSD diagnosis. Secondary analyses suggested that the prediction of PTSD by GrimAge was driven by worse trajectories for intrusive memories and nightmares. Advanced ED GrimAge was also associated with reduced volume of the whole amygdala and specific amygdala subregions, including the cortico-amygdaloid transition and the cortical and accessory basal nuclei. Our findings shed new light on the relation between biological aging and trauma-related phenotypes, suggesting that GrimAge measured at the time of trauma predicts PTSD trajectories and is associated with relevant brain alterations. Furthering these findings has the potential to enhance early prevention and treatment of posttraumatic psychiatric sequelae.

Lack of structural brain alterations associated with insomnia: findings from the ENIGMA-Sleep Working Group.

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5[13.9]years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend,University MedicineGreifswald,Greifswald,Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia.

Apathy in Patients With Cerebral Amyloid Angiopathy: A Multimodal Neuroimaging Study.

To analyze the prevalence and associated clinical characteristics of apathy in sporadic cerebral amyloid angiopathy and investigate whether apathy was associated with disease burden and disconnections of key structures in the reward circuit through a structural and functional multimodal neuroimaging approach. Thirty-seven participants with probable sporadic cerebral amyloid angiopathy without symptomatic intracranial hemorrhage or dementia (mean age, 73.3 ± 7.2 years, % male = 59.5%) underwent a detailed neuropsychological evaluation, including measures of apathy and depression, and a multimodal MR neuroimaging study. A multiple linear regression analysis was used to assess the association of apathy with conventional small vessel disease neuroimaging markers. A voxel-based morphometry with a small volume correction within regions previously associated with apathy and a whole-brain tract-based spatial statistics were performed to identify differences in the gray matter and white matter between the apathetic and nonapathetic groups. Gray matter regions significantly associated with apathy were further evaluated for their functional alterations as seeds in the seed-based resting-state functional connectivity analysis. Potential confounders, namely, age, sex, and measures of depression, were entered as covariates in all analyses. A higher composite small vessel disease marker score (CAA-SVD) was associated with a higher degree of apathy (standardized coefficient = 1.35 (0.07-2.62), adjusted R2 = 27.90, p = 0.04). Lower gray matter volume of the bilateral orbitofrontal cortices was observed in the apathetic group than in the nonapathetic group (F = 13.20, family-wise error-corrected p = 0.028). The apathetic group demonstrated a widespread decrease in white matter microstructural integrity compared with the nonapathetic group. These tracts connect key regions within and between related reward circuits. Finally, there were no significant functional alterations between the apathetic and nonapathetic groups. Our findings revealed the orbitofrontal cortex as a key region in the reward circuit associated with apathy in sporadic cerebral amyloid angiopathy, independent from depression. Apathy was shown to be associated with a higher CAA-SVD score and an extensive disruption of white matter tracts, which suggested that a higher burden of CAA pathology and the disruption in large-scale white matter networks may underlie manifestations of apathy.

Assessing Flexibility of Solution Strategy: Strategy Shifting as a Measure of Cognitive Reserve.

This series of experiments explores whether flexibility in strategy shifting might function as an expression of cognitive reserve (CR). A reasoning task was designed using matrix reasoning stimuli that each required 1 of 2 specific solution strategies: logicoanalytic and visuospatial. It was formatted as a task-switching paradigm, assessing the ability to switch between solution strategies as measured by switch costs. Study 1 was done on Amazon Mechanical Turk and included an assessment of CR proxies. Study 2 used participants who had been studied extensively with neuropsychological assessments and structural neuroimaging. Study 1 found that switch costs increased with aging. In addition, a relationship between switch costs and CR proxies was noted, suggesting a relationship between the flexibility of strategy shifting and CR. The results of Study 2 again indicated that age negatively affected strategy-shifting flexibility, but that individuals with higher CR as measured with standard proxies performed better. The flexibility measure accounted for additional variance in cognitive performance over that explained by cortical thickness, suggesting that it may contribute to CR. Overall, the results are consistent with the idea that flexibility in strategy shifting might be 1 cognitive process that underlies cognitive reserve.

Predicting unipolar and bipolar depression using inflammatory markers, neuroimaging and neuropsychological data: a machine learning study

IntroductionAbout 60% of bipolar disorder (BD) cases are initially misdiagnosed as major depressive disorder (MDD), preventing BD patients from receiving appropriate treatment. An urgency exists to identify reliable biomarkers for improving differential diagnosis (DD). Machine learning methods may help translate current knowledge on biomarkers of mood disorders into clinical practice by providing individual-level classification. No study so far has combined biological data with clinical data to provide a multifactorial predictive model for DD.ObjectivesDefine a predictive algorithm for BD and MDD by integrating structural neuroimaging and inflammatory data with neuropsychological measures (NM). Two different algorithms were compared: multiple kernel learning (MKL) and elastic net regularized logistic regression (EN).MethodsIn a sample of 141 subjects (70 MDD; 71 BD), two different models were implemented for each algorithm: 1) structural neuroimaging measures only (i.e. voxel-based morphometry (VBM), white matter fractional anisotropy (FA), and mean diffusivity (MD)); 2) VBM, FA, and MD combined with NM. In a subsample of 71 subjects (36 BD; 38 MDD), two similar models were implemented: 1) VBM, FA, and, MD combined with only NM; 2) VBM, FA, and MD combined with NM and peripheral inflammatory markers. Finally, the best model was selected for comparison with healthy controls (HC).ResultsOverall, the EN model based on all the modalities achieved the highest accuracy (AUC = 90.2%), outperforming MKL (AUC=85%). EN correctly classified BD and MDD with a diagnostic accuracy of 78.3%, sensitivity of 75%, and specificity of 81.6%. The most significant predictors of BD (variable inclusion probability (VIP) &gt; 80%) were the parahippocampal cingulate, interleukin 9, chemokine CCL5, posterior thalamic radiation, and internal capsule, whereas MDD was best predicted by chemokine CCL23, the anterior cerebellum, and the sagittal stratum. In contrast, NM did not help to differentiate between MDD and BD. However, they help to distinguish patients from HC. Psychomotor coordination and speed of information processing discriminated between MDD and HC (VIP&gt;90%), whereas fluency, working memory, and executive functions differentiated between BD and HC (VIP&gt;80%).ConclusionsIn summary, BD was predicted by a strong proinflammatory profile, whereas MDD was identified by structural neuroimaging data. A multimodal approach offers additional instruments to improve personalized diagnosis in clinical practice and enhance the ability to make DD.Disclosure of InterestNone Declared

Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach

IntroductionEvery year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD.ObjectivesThe present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA).Methods91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC).ResultsModels’ performances are summarized in Table 1.Table 1.Models’ performances.Input featuresAlgorithmSpecificitySensitivityBalanced accuracyAUCVBMSVM55.00%50.00%52.50%0.55FASVM72.00%54.00%63.00%0.62VBM and FAMKL68.00%54.00%61.00%0.58Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry.ConclusionsOverall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients.Disclosure of InterestNone Declared

The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis

The fields of developmental psychopathology, developmental neuroscience, and behavioral genetics are increasingly moving toward a data sharing model to improve reproducibility, robustness, and generalizability of findings. This approach is particularly critical for understanding attention-deficit/hyperactivity disorder (ADHD), which has unique public health importance given its early onset, high prevalence, individual variability, and causal association with co-occurring and later developing problems. A further priority concerns multi-disciplinary/multi-method datasets that can span different units of analysis. Here, we describe a public dataset using a case-control design for ADHD that includes: multi-method, multi-measure, multi-informant, multi-trait data, and multi-clinician evaluation and phenotyping. It spans > 12 years of annual follow-up with a lag longitudinal design allowing age-based analyses spanning age 7–19 + years with a full age range from 7 to 21. Measures span genetic and epigenetic (DNA methylation) array data; EEG, functional and structural MRI neuroimaging; and psychophysiological, psychosocial, clinical and functional outcomes data. The resource also benefits from an autism spectrum disorder add-on cohort and a cross sectional case-control ADHD cohort from a different geographical region for replication and generalizability. Datasets allowing for integration from genes to nervous system to behavior represent the “next generation” of researchable cohorts for ADHD and developmental psychopathology.

Neuroimaging-Derived Predicted Brain Age and Alcohol Use Among Community-Dwelling Older Adults

ObjectivesObservational studies have suggested that moderate alcohol use is associated with reduced risk of dementia. However, the nature of this association is not understood. We investigated whether light to moderate alcohol use may be associated with slower brain aging, among a cohort of older community-dwelling adults using a biomarker of brain age based on structural neuroimaging measures. DesignCross-sectional observational study. ParticipantsWell-characterized members of a longitudinal cohort study who underwent neuroimaging. We categorized the 163 participants (mean age 76.7 ± 7.7, 60% women) into current nondrinkers, light drinkers (1–7 drinks/week) moderate drinkers (>7–14 drinks/week), or heavier drinkers (>14 drinks/week). MeasurementsWe calculated brain-predicted age using structural MRIs processed with the BrainAgeR program, and calculated the difference between brain-predicted age and chronological age (brain-predicted age difference, or brain-PAD). We used analysis of variance to determine if brain-PAD differed across alcohol groups, controlling for potential confounders. ResultsBrain-PAD differed across alcohol groups (F[3, 150] = 4.02; p = 0.009) with heavier drinkers showing older brain-PAD than light drinkers (by about 6 years). Brain-PAD did not differ across light, moderate, and nondrinkers. Similar results were obtained after adjusting for potentially mediating health-related measures, and after excluding individuals with a history of heavier drinking. DiscussionAmong this sample of healthy older adults, consumption of more than 14 drinks/week was associated with a biomarker of advanced brain aging. Light and moderate drinking was not associated with slower brain aging relative to non-drinking.

Alzheimer's Disease: Biophysics, Genetics, Epigenetics, Neuroimaging, Bioelementology, Nutriciology, Treatment, Prevention and Neurotrenching

The complex more than Alzheimer’s disease 115-year-old problem paradigm is an author’s multidisciplinary response through the prism of fundamental applied algorithms/tools/technologies to the pathogenesis, diagnosis, treatment and prevention of this neurodegeneration. Homo sapiens brains are biological, biophysical, neurophysiological and medico-social information exchange paradigms. The achievement of research N. P. Romanchuk is the establishment of many genetic and epigenetic factors of cognitive decline and neurodegenerative diseases. The introduction of copyright developments in the last 15 years has made it possible to form a system of algorithms and tools for managing neuroplasticity. Modified combined EEG/PET and PET/fMRI methods and hybrid PET/CT/MRI technologies are combined functional and structural neuroimaging. Modern communications are multilevel, multiparadigmal, and interdisciplinary models of information exchange. Neurogenetics is a center for multidisciplinary and interagency research using best practices, involving 5P Medicine and 5G technology. In studies, N.P. Romanchuk showed that for the new neurogenesis and neuroplasticity, for the management of human neuroplasticity and biological age, for the modern neurophysiology and neurorehabilitation of cognitive disorders and cognitive disorders, sufficient functional and energy nutrition of the brain is necessary using modern neurotechnologies of nuclear medicine. Modern artificial intelligence technologies are capable of many things, including predicting cognitive impairment and cognitive disorders, using combined and hybrid neuroimaging, sequencing of a new generation, etc., in order to begin timely and effective rehabilitation of the H. sapiens brain. The brain of H. sapiens is the next frontier for healthcare. The merger of combined and hybrid neuroimaging methods with artificial intelligence technologies allows us to understand and diagnose neurological disorders and find new methods of rehabilitation and medical and social support that will lead to improved mental health. The severity of cognitive impairment largely depends on the time of early prevention, the severity of depressive disorder, the age of the patient, neuroendocrine, cerebral and cerebrovascular pathology.