Neuroimaging Measures Research Articles

Plasma Levels of Tissue-Type Plasminogen Activator (tPA) in Normal Aging and Alzheimer's Disease: Links With Cognition, Brain Structure, Brain Function and Amyloid Burden.

Tissue-type plasminogen activator (tPA) is a protease known for its fibrinolytic action but is also involved in physiological and pathophysiological aging processes; including amyloid elimination and synaptic plasticity. The aim of the study was to investigate the role of tPA in cognitive and brain aging. Therefore, we assessed the links between tPA plasma concentration and cognition, structural MRI, FDG-PET and Flobetapir-PET neuroimaging in 155 cognitively unimpaired adults (CUA, aged 20-85 years old) and 32 patients with Alzheimer's disease (ALZ). A positive correlation was found between tPA and age in CUA (p < 0.001), with males showing higher tPA than females (p = 0.05). No significant difference was found between ALZ patients and cognitively unimpaired elders (CUE). Plasma tPA in CUA negatively correlated with global brain volume. No correlation was found with brain FDG metabolism or amyloid deposition. Age-related tPA changes were associated to changes in blood pressure, glycemia and body mass index. Within the ALZ patients, tPA didn't correlate with any cognitive or neuroimaging measures, but only with physiological measures. Altogether our study suggests that increased tPA plasma concentration with age is related to neuronal alterations and cardiovascular risk factors.

Brain health correlates of mobility-related confidence.

Brain health correlates of mobility-related confidence.

Combining MRI and cognitive evaluation to classify concussion in university athletes.

Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R2 = 0.32, p < .001), and performed worse on tests of concentration (∆R2 = 0.07, p < .05) and delayed memory (∆R2 = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.

Type 2 diabetes mellitus accelerates brain aging and cognitive decline: Complementary findings from UK Biobank and meta-analyses.

Type 2 diabetes mellitus (T2DM) is known to be associated with neurobiological and cognitive deficits; however, their extent, overlap with aging effects, and the effectiveness of existing treatments in the context of the brain are currently unknown. We characterized neurocognitive effects independently associated with T2DM and age in a large cohort of human subjects from the UK Biobank with cross-sectional neuroimaging and cognitive data. We then proceeded to evaluate the extent of overlap between the effects related to T2DM and age by applying correlation measures to the separately characterized neurocognitive changes. Our findings were complemented by meta-analyses of published reports with cognitive or neuroimaging measures for T2DM and healthy controls (HCs). We also evaluated in a cohort of T2DM-diagnosed individuals using UK Biobank how disease chronicity and metformin treatment interact with the identified neurocognitive effects. The UK Biobank dataset included cognitive and neuroimaging data (N = 20,314), including 1012 T2DM and 19,302 HCs, aged between 50 and 80 years. Duration of T2DM ranged from 0 to 31 years (mean 8.5 ± 6.1 years); 498 were treated with metformin alone, while 352 were unmedicated. Our meta-analysis evaluated 34 cognitive studies (N = 22,231) and 60 neuroimaging studies: 30 of T2DM (N = 866) and 30 of aging (N = 1088). Compared to age, sex, education, and hypertension-matched HC, T2DM was associated with marked cognitive deficits, particularly in executive functioning and processing speed. Likewise, we found that the diagnosis of T2DM was significantly associated with gray matter atrophy, primarily within the ventral striatum, cerebellum, and putamen, with reorganization of brain activity (decreased in the caudate and premotor cortex and increased in the subgenual area, orbitofrontal cortex, brainstem, and posterior cingulate cortex). The structural and functional changes associated with T2DM show marked overlap with the effects correlating with age but appear earlier, with disease duration linked to more severe neurodegeneration. Metformin treatment status was not associated with improved neurocognitive outcomes. The neurocognitive impact of T2DM suggests marked acceleration of normal brain aging. T2DM gray matter atrophy occurred approximately 26% ± 14% faster than seen with normal aging; disease duration was associated with increased neurodegeneration. Mechanistically, our results suggest a neurometabolic component to brain aging. Clinically, neuroimaging-based biomarkers may provide a valuable adjunctive measure of T2DM progression and treatment efficacy based on neurological effects. The research described in this article was funded by the W. M. Keck Foundation (to LRMP), the White House Brain Research Through Advancing Innovative Technologies (BRAIN) Initiative (NSFNCS-FR 1926781 to LRMP), and the Baszucki Brain Research Fund (to LRMP). None of the funding sources played any role in the design of the experiments, data collection, analysis, interpretation of the results, the decision to publish, or any aspect relevant to the study. DJW reports serving on data monitoring committees for Novo Nordisk. None of the authors received funding or in-kind support from pharmaceutical and/or other companies to write this article.

Multimodal magnetic resonance neuroimaging measures characteristic of early cART-treated pediatric HIV: A feature selection approach.

Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV‐related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (1H‐MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic‐net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1H‐MRS in basal measures to sMRI volumes produced the highest classification performance validation accuracy=85%AUC=0.80. The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART‐treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.

Utility of an experimental medicine model to evaluate efficacy, side-effects and mechanism of action of novel treatments for obesity and binge-eating disorder

Utility of an experimental medicine model to evaluate efficacy, side-effects and mechanism of action of novel treatments for obesity and binge-eating disorder

Brain Gray Matter Atrophy and Functional Connectivity Remodeling in Patients With Chronic LHON.

PurposeThe aim of this study was to investigate the brain gray matter volume (GMV) and spontaneous functional connectivity (FC) changes in patients with chronic Leber's hereditary optic neuropathy (LHON), and their relations with clinical measures.MethodsA total of 32 patients with chronic LHON and matched sighted healthy controls (HC) underwent neuro-ophthalmologic examinations and multimodel magnetic resonance imaging (MRI) scans. Voxel-based morphometry (VBM) was used to detect the GMV differences between the LHON and HC. Furthermore, resting-state FC analysis using the VBM-identified clusters as seeds was carried out to detect potential functional reorganization in the LHON. Finally, the associations between the neuroimaging and clinical measures were performed.ResultsThe average peripapillary retinal nerve fiber layer (RNFL) thickness of the chronic LHON was significantly thinner (T = −16.421, p < 0.001), and the mean defect of the visual field was significantly higher (T = 11.28, p < 0.001) than the HC. VBM analysis demonstrated a significantly lower GMV of bilateral calcarine gyri (CGs) in the LHON than in the HC (p < 0.05). Moreover, in comparison with the HC, the LHON had significantly lower FC between the centroid of the identified left CG and ipsilateral superior occipital gyrus (SOG) and higher FC between this cluster and the ipsilateral posterior cingulate gyrus (p < 0.05, corrected). Finally, the GMV of the left CG was negatively correlated with the LHON duration (r = −0.535, p = 0.002), and the FC between the left CG and the ipsilateral posterior cingulate gyrus of the LHON was negatively correlated with the average peripapillary RNFL thickness (r = −0.522, p = 0.003).ConclusionThe atrophied primary visual cortex of the chronic LHON may be caused by transneuronal degeneration following the retinal damage. Moreover, our findings suggest that the functional organization of the atrophied primary visual cortex has been reshaped in the chronic LHON.

Association of plasma GFAP with neuroimaging measures of amyloid, tau, neurodegeneration and vascular pathology (P4-3.001)

Association of plasma GFAP with neuroimaging measures of amyloid, tau, neurodegeneration and vascular pathology (P4-3.001)

Predicting Cognitive Impairment Using a Data-Driven Cortical Vulnerability Index

Predicting Cognitive Impairment Using a Data-Driven Cortical Vulnerability Index

Structured sparse multiset canonical correlation analysis of simultaneous fNIRS and EEG provides new insights into the human action-observation network

The action observation network (AON) is a network of brain regions involved in the execution and observation of a given action. The AON has been investigated in humans using mostly electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), but shared neural correlates of action observation and action execution are still unclear due to lack of ecologically valid neuroimaging measures. In this study, we used concurrent EEG and functional Near Infrared Spectroscopy (fNIRS) to examine the AON during a live-action observation and execution paradigm. We developed structured sparse multiset canonical correlation analysis (ssmCCA) to perform EEG-fNIRS data fusion. MCCA is a generalization of CCA to more than two sets of variables and is commonly used in medical multimodal data fusion. However, mCCA suffers from multi-collinearity, high dimensionality, unimodal feature selection, and loss of spatial information in interpreting the results. A limited number of participants (small sample size) is another problem in mCCA, which leads to overfitted models. Here, we adopted graph-guided (structured) fused least absolute shrinkage and selection operator (LASSO) penalty to mCCA to conduct feature selection, incorporating structural information amongst the variables (i.e., brain regions). Benefitting from concurrent recordings of brain hemodynamic and electrophysiological responses, the proposed ssmCCA finds linear transforms of each modality such that the correlation between their projections is maximized. Our analysis of 21 right-handed participants indicated that the left inferior parietal region was active during both action execution and action observation. Our findings provide new insights into the neural correlates of AON which are more fine-tuned than the results from each individual EEG or fNIRS analysis and validate the use of ssmCCA to fuse EEG and fNIRS datasets.

Prediction of Post Stroke recovery: Artificial intelligence could be a key of success.

Madam, Stroke is a leading cause of death and disability around the globe and particularly in low- and middle-income countries, and this burden is increasing. (1) Its incidence in Pakistan, is also increasing daily and shares a significant burden by contributing to an exponential expenditure of resources, finances, community manpower, health services and overall economy. (2) Overall disability burden can be reduced remarkably, if early recovery prediction can be formulated for stroke parameters such as upper limb impairment, swallowing, Shoulder Abduction and Finger Extension (SAFE) score, Motor Evoked Potential (MEP) status, National Institute of Health Stroke Scale (NIHSS) scoring. Existing relevant evidences for the early prediction of stroke recovery, reported the use of blood biomarker as an objective indicator. And among them, some serve as a guide in decision-making for clinical practice, such as: Brain natriuretic peptide (BNP), D-Dimer, and have potential in improving the diagnosis and the management of patients with stroke. MRI findings have also made an accurate prognosis about behavioral outcomes after stroke based on the severity of cognitive impairments. (3) For predicting recovery after stroke, various algorithms approaches have also been done since last 10 years and among Predict Recovery Potential (PREP2), (GRAVo) and (PRESS) models of prediction, studies have supported that the PREP2 algorithm was regarded as potentially valid. To date, only one approach has combined biomarkers within the first few days after stroke to make predictions for individual patients. The Predict Recovery Potential (PREP) algorithm predicts upper-limb functional outcomes by combining biomarkers, neurophysiological and neuroimaging measures to make a prognosis. PREP2 algorithm is probably the easiest approach to operationalize among predictive models and serves as a benchmark for predicting motor recovery after stroke. (4) So, till now, there is no consensus among both clinicians and scientists on how to apply a specific predictive model in clinical routine or research protocols, in which biological and psycho-social factors can be collectively incorporated with Artificial intelligence. Hence these steps are mandatory to be implemented in predictive models considering all the factors mentioned above- and including other factors like cost, knowledge, interface development, resources, time and expertise of both scientists and clinicians. ---Continue

Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study).

BackgroundThe digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies.ObjectiveWe aimed to investigate the association between dCDT features and brain volume.MethodsThis study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact.ResultsA total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897.ConclusionsdCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.

Structural neuroimaging measures and lifetime depression across levels of phenotyping in UK biobank

Depression is assessed in various ways in research, with large population studies often relying on minimal phenotyping. Genetic results suggest clinical diagnoses and self-report measures of depression show some core similarities, but also important differences. It is not yet clear how neuroimaging associations depend on levels of phenotyping. We studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging and lifetime depression data. Past depression phenotypes included a single-item self-report measure, an intermediate measure of ‘probable’ lifetime depression, derived from multiple questionnaire items relevant to a history of depression, and a retrospective clinical diagnosis according to DSM-IV criteria. We tested (i) associations between brain structural measures and each depression phenotype, and (ii) effects of phenotype on these associations. Depression-brain structure associations were small (β < 0.1) for all phenotypes, but still significant after FDR correction for many regional metrics. Lifetime depression was consistently associated with reduced white matter integrity across phenotypes. Cortical thickness showed negative associations with Self-reported Depression in particular. Phenotype effects were small across most metrics, but significant for cortical thickness in most regions. We report consistent effects of lifetime depression in brain structural measures, including reduced integrity of thalamic radiations and association fibres. We also observed significant differences in associations with cortical thickness across depression phenotypes. Although these results did not relate to level of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

The Structural and Functional Correlates of Frailty in Persons With Human Immunodeficiency Virus.

Persons with HIV (PWH) are at increased risk of frailty, a clinically recognizable state of increased vulnerability resulting from aging-associated decline in multiple physiologic systems. Frailty is often defined by the Fried criteria, which includes subjective and objective standards concerning health resiliency. However, these frailty metrics do not incorporate cognitive performance or neuroimaging measures. We compared structural (diffusion tensor imaging [DTI]) and functional (cerebral blood flow [CBF]) neuroimaging markers in PWH with frailty and cognitive performance. Virologically controlled PWH were dichotomized as either frail (≥3) or nonfrail (<3) using the Fried criteria. Cognitive Z-scores, both domain (executive, psychomotor speed, language, and memory) and global, were derived from a battery of tests. We identified three regions of reduced CBF, based on a voxel-wise comparison of frail PWH compared with nonfrail PWH. These clusters (bilateral frontal and posterior cingulate) were subsequently used as seed regions of interest (ROIs) for DTI probabilistic white matter tractography. White matter integrity connecting the ROIs was significantly decreased in frail compared with nonfrail PWH. No differences in cognition were observed between frail and nonfrail PWH. However, reductions in white matter integrity among these ROIs was significantly associated with worse psychomotor speed and executive function across the entire cohort. We conclude that frailty in PWH can lead to structural and functional brain changes, including subtle changes that are not detectable by standard neuropsychological tests. Multimodal neuroimaging in conjunction with frailty assessment could identify pathological brain changes observed in PWH.

Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders (MIND-SET): Protocol for a Cross-sectional Comorbidity Study From a Research Domain Criteria Perspective.

It is widely acknowledged that comorbidity between psychiatric disorders is common. Shared and diverse underpinnings of psychiatric disorders cannot be systematically understood based on symptom-based categories of mental disorders, which map poorly onto pathophysiological mechanisms. In the Measuring Integrated Novel Dimensions in Neurodevelopmental and Stress-Related Mental Disorders (MIND-SET) study, we make use of current concepts of comorbidity that transcend the current diagnostic categories. We test this approach to psychiatric problems in patients with frequently occurring psychiatric disorders and their comorbidities (excluding psychosis). The main aim of the MIND-SET project is to determine the shared and specific mechanisms of neurodevelopmental and stress-related psychiatric disorders at different observational levels. This is an observational cross-sectional study. Data from different observational levels as defined in the Research Domain Criteria (genetics, physiology, neuropsychology, system-level neuroimaging, behavior, self-report, and experimental neurocognitive paradigms) are collected over four time points. Included are adult (aged ≥18 years), nonpsychotic, psychiatric patients with a clinical diagnosis of a stress-related disorder (mood disorder, anxiety disorder, or substance use disorder) or a neurodevelopmental disorder (autism spectrum disorder or attention-deficit/hyperactivity disorder). Individuals with no current or past psychiatric diagnosis are included as neurotypical controls. Data collection started in June 2016 with the aim to include a total of 650 patients and 150 neurotypical controls by 2021. The data collection procedure includes online questionnaires and three subsequent sessions with (1) standardized clinical examination, physical examination, and blood sampling; (2) psychological constructs, neuropsychological tests, and biological marker sampling; and (3) neuroimaging measures. We aim to include a total of 650 patients and 150 neurotypical control participants in the time period between 2016 and 2022. In October 2021, we are at 95% of our target. The MIND-SET study enables us to investigate the mechanistic underpinnings of nonpsychotic psychiatric disorders transdiagnostically. We will identify both shared and disorder-specific markers at different observational levels that can be used as targets for future diagnostic and treatment approaches.

Neuroimaging Correlates of Depression after Traumatic Brain Injury: A Systematic Review.

Depression is the most frequent neuropsychiatric complication after traumatic brain injury (TBI) and is associated with poorer outcomes. Neuroimaging has the potential to improve our understanding of the neural correlates of depression after TBI and may improve our capacity to accurately predict and effectively treat this condition. We conducted a systematic review of structural and functional neuroimaging studies that examined the association between depression after TBI and neuroimaging measures. Electronic searches were conducted in four databases and were complemented by manual searches. In total, 2035 citations were identified and, ultimately, 38 articles were included, totaling 1793 individuals (median [25-75%] sample size of 38.5 [21.8-54.3] individuals). The most frequently used modality was structural magnetic resonance imaging (MRI) (n = 17, 45%), followed by diffusion tensor imaging (n = 11, 29%), resting-state functional MRI (n = 10, 26%), task-based functional MRI (n = 4, 8%), and positron emission tomography (n = 2, 4%). Most studies (n = 27, 71%) were cross-sectional. Overall, depression after TBI was associated with lower gray matter measures (volume, thickness, and/or density) and greater white matter damage. However, identification of specific brain areas was somewhat inconsistent. Findings that were replicated in more than one study included reduced gray matter in the rostral anterior cingulate cortex, pre-frontal cortex, and hippocampus, and damage in five white matter tracts (cingulum, internal capsule, superior longitudinal fasciculi, and anterior and posterior corona radiata). This systematic review found that the available data did not converge on a clear neuroimaging biomarker for depression after TBI. However, there are promising targets that warrant further study.

The cognitive profile of Friedreich ataxia: a systematic review and meta-analysis

BackgroundStudy the cognitive profile of individuals with Friedreich ataxia (FRDA) and seek evidence for correlations between clinical, genetic and imaging characteristics and neuropsychological impairments.MethodsBased on PRISMA guidelines, a meta-analysis was realized using the Pubmed and Scopus databases to identify studies (1950–2021) reporting neuropsychological test results in genetically confirmed FRDA and control participants in at least one of the following cognitive domains: attention/executive, language, memory and visuo-spatial functions as well as emotion. Studies using identical outcomes in a minimum of two studies were pooled. Pooled effect sizes were calculated with Cohen’s d.ResultsEighteen studies were included. Individuals with FRDA displayed significantly lower performance than individuals without FRDA in most language, attention, executive function, memory visuospatial function, emotion regulation and social cognitive tasks. Among the included studies, thirteen studies examined the relationship between neuropsychological test results and clinical parameters and reported significant association with disease severity and six studies reviewed the relationship between neuroimaging measures and cognitive performance and mainly reported links between reduced cognitive performance and changes in cerebellar structure.ConclusionsIndividuals with FRDA display significantly lower performances in many cognitive domains compared to control participants. The spectrum of the cognitive profile alterations in FRDA and its correlation with disease severity and cerebellar structural parameters suggest a cerebellar role in the pathophysiology of FRDA cognitive impairments.

Learning about the Experience of Amyloid imaging and early RecogNition of Alzheimer's Disease in Veterans and Their Caregivers: Overview of the LEARN-AD Study

Learning about the Experience of Amyloid imaging and early RecogNition of Alzheimer's Disease in Veterans and Their Caregivers: Overview of the LEARN-AD Study

State anxiety may not affect memory encoding, retrieval, or recognition in older adults with mood disorders

State anxiety may not affect memory encoding, retrieval, or recognition in older adults with mood disorders

Soluble TREM2 in CSF and its association with other biomarkers and cognition in autosomal-dominant Alzheimer's disease: a longitudinal observational study

Soluble TREM2 in CSF and its association with other biomarkers and cognition in autosomal-dominant Alzheimer's disease: a longitudinal observational study