Alterations in cortical thickness and white matter integrity in mild cognitive impairment measured by whole-brain cortical thickness mapping and diffusion tensor imaging

Gamma oscillation modulation for cognitive impairment: A systematic review

BackgroundGamma oscillation modulation has emerged as a potential non-invasive treatment to counteract cognitive impairment in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Non-invasive brain stimulation techniques like transcranial alternating current stimulation (tACS), gamma sensory stimulation (GSS), and transcranial magnetic stimulation (TMS) show promise in supporting specific cognitive functions.ObjectiveTo review and evaluate the efficacy of gamma oscillation modulation techniques in benefiting cognitive functions among individuals with AD and MCI.MethodsA systematic review was conducted, analyzing studies from 2015 to 2023 across databases such as PubMed, Web of Science, and Scopus. Inclusion criteria focused on studies involving tACS, GSS, or TMS applied to older adults with MCI or AD. A total of 438 articles were screened, of which 10 met the eligibility criteria.ResultsFindings suggest that gamma tACS, especially targeting the precuneus and dorsolateral prefrontal cortex, benefits episodic memory and cognitive performance. GSS also showed potential in supporting memory and attention, while TMS exhibited inconsistent but promising results when applied to the angular gyrus. However, heterogeneity in study designs and small sample sizes limit the generalizability of these outcomes.ConclusionsGamma oscillation modulation offers potential cognitive benefits for patients with AD and MCI, particularly in memory support. Further studies with larger samples and well-designed protocols are needed to confirm its therapeutic efficacy and optimize intervention parameters.

The hippocampus as a structural and functional network epicentre for distant cortical thinning in neurocognitive aging

Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ± 8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (β=-0.150,p=<.001) and RHIP (β=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (β=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (β=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (β=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (β=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (β=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.

Presence of Auditory Pathway Abnormalities in Children With Neurofibromatosis Type 1 With Brainstem Focal Areas of Abnormal Signal Intensity: Diffusion Tensor Imaging Features

Background: To investigate whether there is a difference in mean diffusivity (MD) and fractional anisotropy (FA) values in the auditory pathways of neurofibromatosis type 1 patients with and without focal areas of abnormal signal intensity (FASI) compared to healthy controls by using diffusion tensor imaging (DTI). Methods: Patients were classified as group 1 with focal areas of abnormal signal intensity in the brainstem, group 2 without focal areas of abnormal signal intensity, and healthy control group 3 according to the MRI findings. Mean diffusivity and fractional anisotropy values of lateral lemniscus, inferior colliculus, corpus geniculatum mediale, Heschl gyrus, and brainstem were compared between groups. The correlation between mean diffusivity and fractional anisotropy values of auditory pathways and age was investigated. Results: There was a significant difference between group 1 and group 2 in terms of mean diffusivity and fractional anisotropy values at lateral lemniscus, inferior colliculus, corpus geniculatum mediale, and Heschl gyrus. Increased mean diffusivity and decreased fractional anisotropy values at brainstem were found in group 1. There was a significant difference between group 1 and group 3 in terms of mean diffusivity values at all auditory pathways. Fractional anisotropy values obtained from lateral lemniscus, inferior colliculus, and Heschl gyrus decreased in group 1 compared with group 3. There was a negative correlation between mean diffusivity values and positive correlation between fractional anisotropy values at lateral lemniscus, inferior colliculus, Heschl gyrus, and age. Conclusions: Our diffusion tensor imaging findings show that the neuronal integrity of the auditory pathways is affected in neurofibromatosis type 1 patients with brainstem focal areas of abnormal signal intensity. We think that the disappearance of brainstem focal areas of abnormal signal intensity associated with myelin repair and the regression of diffusion tensor imaging changes in the auditory pathways occur simultaneously with advancing age in patients with neurofibromatosis type 1.

Perturbation-based dual task assessment in older adults with mild cognitive impairment

Background

Dual tasking (i.e., concurrent performance of motor and cognitive task) is significantly impaired in older adults with mild cognitive impairment (OAwMCI) compared to cognitively intact older adults (CIOA) and has been associated with increased fall risk. Dual task studies have primarily examined volitionally driven events, and the effects of mild cognitive impairment on reactive balance control (i.e., the ability to recover from unexpected balance threats) are unexplored. We examined the effect of cognitive tasks on reactive balance control in OAwMCI compared to CIOA.

Methods

Adults >55 years were included and completed the Montreal Cognitive Assessment (MoCA) to categorize them as OAwMCI (MoCA: 18-24, n = 15) or CIOA (MoCA: ≥25, n = 15). Both OAwMCI [MoCA: 22.4 (2.2), 65.4 (6.1) years, 3 females] and CIOA [MoCA: 28.4 (1.3), 68.2 (5.5) years, 10 females] responded to large magnitude stance slip-like perturbations alone (single task) and while performing perceptual cognitive tasks targeting the visuomotor domain (target and tracking game). In these tasks, participants rotated their head horizontally to control a motion mouse and catch a falling target (target game) or track a moving object (track). Margin of stability (MOS) and fall outcome (harness load cell >30% body weight) were used to quantify reactive balance control. Cognitive performance was determined using performance error (target) and sum of errors (tracking). A 3 × 2 repeated measures ANOVA examined the effect of group and task on MOS, and generalized estimating equations (GEE) model was used to determine changes in fall outcome between groups and tasks. 2 × 2 repeated measures ANOVAs examined the effect of group and task on cognitive performance.

Results

Compared to CIOA, OAwMCI exhibited significantly deteriorated MOS and greater number of falls during both single task and dual task (p < 0.05), and lower dual task tracking performance (p < 0.01). Compared to single task, both OAwMCI and CIOA exhibited significantly deteriorated perceptual cognitive performance during dual task (p < 0.05); however, no change in MOS or fall outcome between single task and dual task was observed.

Conclusion

Cognitive impairment may diminish the ability to compensate and provide attentional resources demanded by sensory systems to integrate perturbation specific information, resulting in deteriorated ability to recover balance control among OAwMCI.

Identifying Leukoaraiosis with Mild Cognitive Impairment by Fusing Multiple MRI Morphological Metrics and Ensemble Machine Learning

Leukoaraiosis (LA) is strongly associated with impaired cognition and increased dementia risk. Determining effective and robust methods of identifying LA patients with mild cognitive impairment (LA-MCI) is important for clinical intervention and disease monitoring. In this study, an ensemble learning method that combines multiple magnetic resonance imaging (MRI) morphological features is proposed to distinguish LA-MCI patients from LA patients lacking cognitive impairment (LA-nCI). Multiple comprehensive morphological measures (including gray matter volume (GMV), cortical thickness (CT), surface area (SA), cortical volume (CV), sulcus depth (SD), fractal dimension (FD), and gyrification index (GI)) are extracted from MRI to enrich model training on disease characterization information. Then, based on the general extreme gradient boosting (XGBoost) classifier, we leverage a weighted soft-voting ensemble framework to ensemble a data-level resampling method (Fusion + XGBoost) and an algorithm-level focal loss (FL)-improved XGBoost model (FL-XGBoost) to overcome class-imbalance learning problems and provide superior classification performance and stability. The baseline XGBoost model trained on an original imbalanced dataset had a balanced accuracy (Bacc) of 78.20%. The separate Fusion + XGBoost and FL-XGBoost models achieved Bacc scores of 80.53 and 81.25%, respectively, which are clear improvements (i.e., 2.33% and 3.05%, respectively). The fused model distinguishes LA-MCI from LA-nCI with an overall accuracy of 84.82%. Sensitivity and specificity were also well improved (85.50 and 84.14%, respectively). This improved model has the potential to facilitate the clinical diagnosis of LA-MCI.

Aerobic exercise attenuates abnormal myelination and oligodendrocyte differentiation in 3xTg-AD mice

Pathological features of Alzheimer's Disease (AD) include alterations in the structure and function of neurons as well as of myelin sheaths. Accumulated evidence shows that aerobic type of exercise can enhance neuroplasticity in mouse models of AD. However, whether and how aerobic exercise can affect myelin sheath repair and neuroprotection in the AD models remains unclear. In this study we tested the hypotheses that 1) myelin structural alterations in 3xTg-AD mice would be related to abnormalities in oligodendrocyte lineage cells, resulting in impaired learning and memory, and 2) a 6-month aerobic exercise intervention would have beneficial effects on such alterations. Two-month-old male 3xTg-AD mice were randomly assigned to a control (AC) or an exercise (AE) group, and age-matched male C57BL/6;129 mice were also randomly assigned to a normal control (NC) or an exercise (NE) group, with n = 12 in each group. Mice in the exercise groups were trained on a motor-drive treadmill, 60 min per day, 5 days per week for 6 months. Cognitive function was assessed at the end of the intervention period. Then, brain specimens were obtained for assessments of morphological and oligodendrocyte lineage cell changes. The results of electron microscopy showed that myelin ultrastructure demonstrated a higher percentage of loose and granulated myelin sheath around axons in the temporal lobe in the AC, as compared with the NC group, along with greater cognitive dysfunction at 8-months of age. These differences were accompanied by significantly greater myelin basic protein (MBP) expression and less neuron-glial antigen-2 (NG2) protein and mRNA levels in the AC, compared to the NC. However, there were no significant between-group differences in the G-ratio (the ratio of axon diameter to axon plus myelin sheath diameter) and 2',3'-cyclic-nucleotide 3'-phosphodiesterase (CNPase) protein and mRNA levels. The aerobic exercise ameliorated cognitive deterioration and appeared to keep components of myelin sheath and oligodendrocyte precursor cells stabilized, resulting in a decrease in the percentage of loose and granulated myelin sheath and MBP protein, and an increase in NG2 protein and mRNA levels in the AE group. Therefore, the 6-month exercise intervention demonstrated beneficial effects on myelin lesions, abnormal differentiation of oligodendrocytes and general brain function in the 3xTg-AD mice, providing further insights into the role of aerobic exercise in management of neurodegeneration in AD by maintaining intact myelination.

White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan

Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.

The relationship between long-term blood pressure variability and cortical thickness in older adults

High blood pressure variability (BPV) is a risk factor for cognitive decline and dementia, but its association with cortical thickness is not well understood. Here we use a topographical approach, to assess links between long-term BPV and cortical thickness in 478 (54% men at baseline) community dwelling older adults (70-88 years) from the ASPirin in Reducing Events in the Elderly NEURO sub-study. BPV was measured as average real variability, based on annual visits across three years. Higher diastolic BPV was significantly associated with reduced cortical thickness in multiple areas, including temporal (banks of the superior temporal sulcus), parietal (supramarginal gyrus, post-central gyrus), and posterior frontal areas (pre-central gyrus, caudal middle frontal gyrus), while controlling for mean BP. Higher diastolic BPV was associated with faster progression of cortical thinning across the three years. Diastolic BPV is an important predictor of cortical thickness, and trajectory of cortical thickness, independent of mean blood pressure. This finding suggests an important biological link in the relationship between BPV and cognitive decline in older age.

Assessment of complementary white matter microstructural changes and grey matter atrophy in a preclinical model of Alzheimer's disease

Alzheimer's disease (AD) has been associated with amyloid and tau pathology, as well as neurodegeneration. Beyond these hallmark features, white matter microstructural abnormalities have been observed using MRI. The objective of this study was to assess grey matter atrophy and white matter microstructural changes in a preclinical mouse model of AD (3xTg-AD) using voxel-based morphometry (VBM) and free-water (FW) diffusion tensor imaging (FW-DTI). Compared to controls, lower grey matter density was observed in the 3xTg-AD model, corresponding to the small clusters in the caudate-putamen, hypothalamus, and cortex. DTI-based fractional anisotropy (FA) was decreased in the 3xTg model, while the FW index was increased. Notably, the largest clusters for both FW-FA and FW index were in the fimbria, with other regions including the anterior commissure, corpus callosum, forebrain septum, and internal capsule. Additionally, the presence of amyloid and tau in the 3xTg model was confirmed with histopathology, with significantly higher levels observed across many regions of the brain. Taken together, these results are consistent with subtle neurodegenerative and white matter microstructural changes in the 3xTg-AD model that manifest as increased FW, decreased FW-FA, and decreased grey matter density.

Association of Indication for Hospitalization With Subsequent Amyloid Positron Emission Tomography and Magnetic Resonance Imaging Biomarkers

BACKGROUND

Hospitalization in older age is associated with accelerated cognitive decline, typically preceded by neuropathologic changes. We assess the association between indication for hospitalization and brain neurodegeneration.

METHODS

Included were participants from the Mayo Clinic Study of Aging, a population-based longitudinal study, with ≥1 brain imaging available in those older than 60 years of age between 2004 and 2017. Primary analyses used linear mixed-effects models to assess association of hospitalization with changes in longitudinal trajectory of cortical thinning, amyloid accumulation, and white matter hyperintensities (WMH). Additional analyses were performed with imaging outcomes dichotomized (normal vs abnormal) using Cox proportional hazards regression.

RESULTS

Of 2 480 participants, 1 966 had no hospitalization and 514 had ≥1 admission. Hospitalization was associated with accelerated cortical thinning (annual slope change -0.003 mm [95% confidence interval (CI) -0.005 to -0.001], p = .002), but not amyloid accumulation (0.003 [95% CI -0.001 to 0.006], p = .107), or WMH increase (0.011 cm3 [95% CI -0.001 to 0.023], p = .062). Interaction analyses assessing whether trajectory changes are dependent on admission type (medical vs surgical) found interactions for all outcomes. While surgical hospitalizations were not, medical hospitalizations were associated with accelerated cortical thinning (-0.004 mm [95% CI -0.008 to -0.001, p = .014); amyloid accumulation (0.010, [95% CI 0.002 to 0.017, p = .011), and WMH increase (0.035 cm3 [95% CI 0.012 to 0.058, p = .006). Hospitalization was not associated with developing abnormal cortical thinning (p = .407), amyloid accumulation (p = .596), or WMH/infarctions score (p = .565).

CONCLUSIONS

Medical hospitalizations were associated with accelerated cortical thinning, amyloid accumulation, and WMH increases. These changes were modest and did not translate to increased risk for crossing the abnormality threshold.

White matter hyperintensities and longitudinal cognitive decline in cognitively normal populations and across diagnostic categories: A meta-analysis, systematic review, and recommendations for future study harmonization

INTRODUCTION

The primary aim of this paper is to improve the clinical interpretation of white matter hyperintensities (WMHs) and provide an overarching summary of methodological approaches, allowing researchers to design future studies targeting current knowledge gaps.

METHODS

A meta-analysis and systematic review was performed investigating associations between baseline WMHs and longitudinal cognitive outcomes in cognitively normal populations, and populations with mild cognitive impairment (MCI), Alzheimer's disease (AD), and stroke.

RESULTS

Baseline WMHs increase the risk of cognitive impairment and dementia across diagnostic categories and most consistently in MCI and post-stroke populations. Apolipoprotein E (APOE) genotype and domain-specific cognitive changes relating to strategic anatomical locations, such as frontal WMH and executive decline, represent important considerations. Meta-analysis reliability was assessed using multiple methods of estimation, and results suggest that heterogeneity in study design and reporting remains a significant barrier.

DISCUSSION

Recommendations and future directions for study of WMHs are provided to improve cross-study comparison and translation of research into consistent clinical interpretation.

Establishing an individualized model of conversion from normal cognition to Alzheimer's disease after 4 years, based on cognitive, brain morphology and neuropsychiatric characteristics

OBJECTIVES

The impact of neuropsychiatric symptoms (NPS) on cognitive performance has been reported, and this impact was better defined in the aging population. Yet the potential of using the impact of NPS on brain and cognitive performance in a longitudinal setting, as prediction of conversion - have remained questionable. This study proposes to establish a predictive model of conversion to Alzheimer's disease (AD) and mild cognitive impairment (MCI) based on current cognitive performance, NPS and their associations with brain morphology.

METHODS

156 participants with MCI from the Alzheimer's Disease Neuroimaging Initiative database cognitively stable after a 4-year follow-up were compared to 119 MCI participants who converted to AD. Each participant underwent a neuropsychological assessment evaluating verbal memory, language, executive and visuospatial functions, a neuropsychiatric inventory evaluation and a 3 Tesla MRI. The statistical analyses consisted of 1) baseline comparison between the groups; 2) analysis of covariance model (controlling demographic parameters including functional abilities) to specify the variables that distinguish the two subgroups and; 3) used the significant ANCOVA variables to construct a binary logistic regression model that generates a probability equation to convert to a lower cognitive performance state.

RESULTS

Results showed that MCI who converted to AD in comparison to stable MCI, exhibited a higher NPS prevalence, a lower cognitive performance and a higher number of involved brain structures. Functional abilities, memory performance and the sizes of inferior temporal, hippocampal and amygdala sizes were significant predictors of MCI to AD conversion. We also report two models of conversion that can be implemented on an individual basis for calculating the percentage risk of conversion after 4 years.

CONCLUSION

These analytical methods might be a good way to anticipate cognitive and brain declines.

The Effect of APOE ɛ4 on the Functional Connectivity in Frontoparietal Network in Hypertensive Patients

Allele 4 of the apolipoprotein E gene (APOE ε4) and hypertension are considered risk factors for Alzheimer’s Disease (AD). The detection of differences in cognitive function and brain networks between hypertensive patients who are APOE ε4 carriers and non-carriers may help in understanding how hypertension and risk genes cumulatively impair brain function, which could provide critical insights into the genetic mechanism by which hypertension serves as a potential risk factor for cognitive decline and even AD. Using behavioral data from 233 elderly hypertensive patients and neuroimaging data from 38 of them from Beijing, China; the study aimed to assess the effects of APOE ε4 on cognition and to explore related changes in functional connectivity. Cognitively, the patients with APOE ε4 showed decreased executive function, memory and language. In the MRI sub-cohort, the frontoparietal networks in the APOE ε4 carrier group exhibited an altered pattern, mainly in the left precentral regions, inferior frontal lobe and angular gyrus. More importantly, the decline of cognitive function was correlated with abnormal FC in the left precentral regions in APOE ε4 carriers. APOE ε4 aggravated the dysfunction in frontal and parietal regions in hypertensive patients. This highlights the importance of brain protection in hypertensive patients, especially those with a genetic risk of AD.

A high-generalizability machine learning framework for predicting the progression of Alzheimer's disease using limited data

Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

Going round in circles-The Papez circuit in Alzheimer's disease

The hippocampus is regarded as the pivotal structure for episodic memory symptoms associated with Alzheimer's disease (AD) pathophysiology. However, what is often overlooked is that the hippocampus is 'only' one part of a network of memory critical regions, the Papez circuit. Other Papez circuit regions are often regarded as less relevant for AD as they are thought to sit 'downstream' of the hippocampus. However, this notion is oversimplistic, and increasing evidence suggests that other Papez regions might be affected before or concurrently with the hippocampus. In addition, AD research has mostly focused on episodic memory deficits, whereas spatial navigation processes are also subserved by the Papez circuit with increasing evidence supporting its valuable potential as a diagnostic measure of incipient AD pathophysiology. In the current review, we take a step forward analysing recent evidence on the structural and functional integrity of the Papez circuit across AD disease stages. Specifically, we will review the integrity of specific Papez regions from at-genetic-risk (APOE4 carriers), to mild cognitive impairment (MCI), to dementia stage of sporadic AD and autosomal dominant AD (ADAD). We related those changes to episodic memory and spatial navigation/orientation deficits in AD. Finally, we provide an overview of how the Papez circuit is affected in AD diseases and their specific symptomology contributions. This overview strengthened the need for moving away from a hippocampal-centric view to a network approach on how the whole Papez circuit is affected in AD and contributes to its symptomology, informing future research and clinical approaches.

Perturbation-based balance assessment: Examining reactive balance control in older adults with mild cognitive impairments

Background

Older adults with mild cognitive impairment (OAwMCI) present subtle balance and gait deficits along with subjective memory decline. Although these presentations might not affect activities of daily living (ADLs), they attribute to a two-folded increase in falls. While changes occurring in volitional balance control during ADLs have been extensively examined among OAwMCI, reactive balance control, required to recover from external perturbations, has received little attention. Therefore, this study examined reactive balance control in OAwMCI compared to their healthy counterparts.

Methods

Fifteen older adults with mild cognitive impairment (OAwMCI), fifteen cognitively intact older adults (CIOA) (>55 years), and fifteen young adults (18-30 years) were exposed to stance perturbations at three different intensities. Behavioral outcomes postural COM state stability, step length, step initiation, and step execution were computed.

Results

Postural COM state stability was the lowest in OAwMCI compared to CIOA and young adults, and it deteriorated at higher perturbation intensities (P < 0.001). Step length was the lowest among OAwMCI and was significantly different from young adults (P < 0.001) but not from CIOA. Unlike OAwMCI, CIOA and young adults increased their step length at higher perturbation intensities (P < 0.001). OAwMCI showed longer recovery step initiation times and shorter execution times compared to CIOA and young adults at higher perturbation intensities (P < 0.001).

Conclusion

OAwMCI exhibit exacerbated reactive instability and are unable to modulate their responses as the threat to balance control altered. Thus, they are at a significantly higher risk of falls than their healthy counterparts.

A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer's Disease

The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer's disease, that is, early signs of subnetworks impairment due to Alzheimer's disease. The sample in this study consisted of 123 first-degree Alzheimer's disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer's disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer's disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer's disease structural-connectome-trait presents a posterior-posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.

Synthetic MRI in Neurofibromatosis Type 1

BACKGROUND AND PURPOSE

Synthetic MRI enables the generation of various contrast-weighted images and quantitative data in a reasonable scanning time. We aimed to use synthetic MRI to assess the detection and underlying tissue characteristics of focal areas of signal intensity and normal-appearing brain parenchyma and morphometric alterations in the brains of patients with neurofibromatosis type 1.

MATERIALS AND METHODS

Conventional MR imaging and synthetic MRI were prospectively obtained from 19 patients with neurofibromatosis type 1 and 18 healthy controls. Two neuroradiologists independently evaluated focal areas of signal intensity on both conventional MR imaging and synthetic MRI. Additionally, automatically segmented volume calculations of the brain in both groups and quantitative analysis of myelin, including the focal areas of signal intensity and normal-appearing brain parenchyma, of patients with neurofibromatosis type 1 were performed using synthetic MRI.

RESULTS

The comparison of conventional MR imaging and synthetic MRI showed good correlation in the supratentorial region of the brain (κ = 0.82-1). Automatically segmented brain parenchymal volume, intracranial volume, and GM volumes were significantly increased in the patients with neurofibromatosis type 1 (P < .05). The myelin-correlated compound, myelin fraction volume, WM fraction volume, transverse relaxation rate, and longitudinal relaxation rate values were significantly decreased in focal areas of signal intensity on myelin and WM maps (P < .001); however, GM, GM fraction volume, and proton density values were significantly increased on the GM map (P < .001).

CONCLUSIONS

Synthetic MRI is a potential tool for the assessment of morphometric and tissue alterations as well as the detection of focal areas of signal intensity in patients with neurofibromatosis type 1 in a reasonable scan time.

Cortical Thickness and Hippocampal Volume in Vascular and Non-vascular Depressed Patients

Background: Reduced cortical thickness and hippocampal volume are prevalent markers of late life depression as well as mild cognitive impairment (MCI) but are conspicuously absent in the vascular depression (VD) literature. The present study aimed to determine differences in cortical thickness and hippocampal volume between VD and non-VD patients. Methods: Participants were enrolled in an 8-week open treatment antidepressant trial. Forty-one depressed individuals aged 50 and older underwent brain magnetic resonance imaging at baseline and were classified as VD or non-VD. Cortical thickness values for the left and right entorhinal, parahippocampal, and precuneal cortices, as well as left and right hippocampal volume, were linearly regressed on VD status to determine mean differences between VD and non-VD. Covariates included site, age, sex, and mean thickness or intracranial volume. Results: No statistical differences were found between VD and non-VD patients in cortical thickness of the bilateral precuneal, entorhinal, or parahippocampal cortices, or hippocampal volume (p > 0.001). Conclusions: The absence of statistical differences in gray matter between VD and non-VD patients raises several diagnostic, etiological, and developmental possibilities, namely that VD may not be connected with other late-life psychiatric illnesses such as MCI or dementia and that vascular disease may not be a common etiological risk factor for depression and dementia. Larger datasets, prospective longitudinal studies, and cognitively intact controls are needed to further address these types of questions.

Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease

Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain age model as biomarker is the prediction of Alzheimer's disease (AD) from structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied to this specific predictive task, some of which have achieved high accuracy at the expense of the descriptiveness of the model. This work investigates an appropriate combination of data science techniques and linear models to provide, at the same time, high accuracy and good descriptiveness. The proposed method is based on a data workflow that include typical data science methods, such as outliers detection, feature selection, linear regression, and logistic regression. In particular, a novel inductive bias is introduced in the regression model, which is aimed at improving the accuracy and the specificity of the classification task. The method is compared to other machine learning approaches for AD classification based on morphological brain data with and without the use of the brain age, including Support Vector Machines and Deep Neural Networks. This study adopts brain MRI scans of 1, 901 subjects which have been acquired from three repositories (ADNI, AIBL, and IXI). A predictive model based only on the proposed apparent brain age and the chronological age has an accuracy of 88% and 92%, respectively, for male and female subjects, in a repeated cross-validation analysis, thus achieving a comparable or superior performance than state of the art machine learning methods. The advantage of the proposed method is that it maintains the morphological semantics of the input space throughout the regression and classification tasks. The accurate predictive model is also highly descriptive and can be used to generate potentially useful insights on the predictions.

Cortical Complexity Analyses and Their Cognitive Correlate in Alzheimer's Disease and Frontotemporal Dementia

Background:

The changes of cortical structure in Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are usually described in terms of atrophy. However, neurodegenerative diseases may also affect the complexity of cortical shape, such as the fractal dimension of the brain surface.

Objective:

In this study, we aimed at assessing the regional patterns of cortical thickness and fractal dimension changes in a cross-sectional cohort of patients with AD and FTD.

Methods:

Thirty-two people with symptomatic AD-pathology (clinically probable AD, n = 18, and amyloid-positive mild cognitive impairment, n = 14), 24 with FTD and 28 healthy controls underwent high-resolution 3T structural brain MRI. Using surface-based morphometry, we created vertex-wise cortical thickness and fractal dimension maps for group comparisons and correlations with cognitive measures in AD and FTD.

Results:

In addition to the well-established pattern of cortical thinning encompassing temporoparietal regions in AD and frontotemporal areas in FTD, we observed reductions of fractal dimension encompassing cingulate areas and insula for both conditions, but specifically involving orbitofrontal cortex and paracentral gyrus for FTD (FDR p < 0.05). Correlational analyses between fractal dimension and cognition showed that these regions were particularly vulnerable with regards to memory and language impairment, especially in FTD.

Conclusion:

While the present study demonstrates globally similar patterns of fractal dimension changes in AD and FTD, we observed distinct cortical complexity correlates of cognitive domains impairment. Further studies are required to assess cortical complexity measures at earlier disease stages (e.g., in prodromal/asymptomatic carriers of FTD-related gene mutations) and determine whether fractal dimension represents a sensitive imaging marker for prevention and diagnostic strategies.

Deficits in saccadic eye movements differ between subtypes of patients with mild cognitive impairment

Introduction: Mild cognitive impairment (MCI) is known to be heterogeneous in its cognitive features and course of progression. Whilst memory impairment is characteristic of amnestic MCI (aMCI), cognitive deficits other than memory can occur in both aMCI and non-amnestic MCI (naMCI) and accurate assessment of the subtypes of MCI is difficult for clinicians without the application of extensive neuropsychological testing. In this study, we examine metrics derived from recording of reflexive and voluntary saccadic eye movements as a potential alternative method for discriminating between subtypes and assessing cognitive functions in MCI.Method: A total of 29 MCI patients and 29 age- and education-matched healthy controls (HCs) participated in the cross-sectional study. We recorded horizontal and vertical pro-saccades and anti-saccade responses. All the participants also completed a comprehensive neuropsychological tests battery.Results: Significant differences in saccadic eye movement were found between the subtypes of MCI and HCs. Patients with aMCI had a higher percentage of short latency "express" saccades than HCs. We found strong associations between saccadic reaction times and cognitive domains, including executive functions and attention. The mini-mental state examination (MMSE) was also found to correlate with uncorrected errors in the anti-saccade task.Conclusions: The increased proportion of saccades in the express latency range in aMCI may be indicative of problems with cognitive inhibitory control in these patients. A focus on this and other saccade metrics in the preclinical and prodromal stages of dementia may help to predict the clinical progression of the disease and direct interventions for the management of MCI. The clinical significance of saccadic eye movement impairments in MCI is not yet fully understood and should be investigated in further studies using larger samples.

Dual-task performance is associated with brain MRI Morphometry in individuals with mild cognitive impairment

BACKGROUND AND PURPOSE

Cognitive impairment is a critical health problem in the elderly population. Research has shown that patients with mild cognitive impairment (MCI) may develop dementia in later years. Therefore, early identification of MCI could allow for interventions to help delay the progression of this devastating disease. Our objective in this study was to detect the early presence of MCI in elderly patients via neuroimaging and dual-task performance.

METHODS

Brain MRI scans from 21 older adult volunteers, including cognitively healthy adults (HA, n = 9, age = 68-79 years) and mild cognitively impaired (MCI, n = 12, age = 66-92 years) were analyzed using automatic segmentation techniques. Regional volume, surface area, and thickness measures were correlated with simultaneous performance of motor and cognitive tasks (dual-task) within a novel upper-extremity function (UEF) test, using multivariate analysis of variance models.

RESULTS

We found significant associations of dual-task performance with volume of five cortical brain regions (P ≤ .048) and thickness of 13 regions (P ≤ .043) within the frontal, temporal, and parietal lobes. There was a significant interaction effect of cognitive group on dual-task score for the inferior temporal gyrus volume (P ≤ .034), and the inferior parietal lobule, inferior temporal gyrus, and middle temporal gyrus average thickness (P ≤ .037).

CONCLUSIONS

This study highlighted the potential of dual-tasking and MRI morphometric changes as a simple and accurate tool for early detection of cognitive impairment among community-dwelling older adults. The strong interaction effects of cognitive group on UEF dual-task score suggest higher association between atrophy of these brain structures and compromised dual-task performance among the MCI group.

Exploring the Relationship between Gray and White Matter in Healthy Adults: A Hybrid Research of Cortical Reconstruction and Tractography

The gray matter (GM) and white matter (WM) are structurally and functionally related in the human brain. Among the numerous neuroimaging studies, yet only a few have investigated these two structures in the same sample. So, there is limited and inconsistent information about how they are correlated in the brain of healthy adults. In this study, we combined cortical reconstruction with diffusion spectrum imaging (DSI) tractography to investigate the relationship between cortical morphology and microstructural properties of major WM tracts in 163 healthy young adults. The results showed that cortical thickness (CTh) was positively correlated with the coherent tract-wise fractional anisotropy (FA) value, and the correlation was stronger in the dorsal areas than in the ventral areas. For other diffusion parameters, CTh was positively correlated with axial diffusivity (AD) of coherent fibers in the frontal areas and negatively correlated with radial diffusivity (RD) of coherent fibers in the dorsal areas. These findings suggest that the correlation between GM and WM is inhomogeneity and could be interpreted with different mechanisms in different brain regions. We hope our research could provide new insights into the studies of diseases in which the GM and WM are both affected.

Diffusion Tensor Imaging Can Discriminate the Primary Cell Type of Intracranial Metastases for Patients with Lung Cancer

PURPOSE

Histopathological differentiation of primary lung cancer is clinically important. We aimed to investigate whether diffusion tensor imaging (DTI) parameters of metastatic brain lesions could predict the histopathological types of the primary lung cancer.

METHODS

In total, 53 patients with 98 solid metastatic brain lesions of lung cancer were included. Lung tumors were subgrouped as non-small cell carcinoma (NSCLC) (n = 34) and small cell carcinoma (SCLC) (n = 19). Apparent diffusion coefficient (ADC) and Fractional anisotropy (FA) values were calculated from solid enhanced part of the brain metastases. The association between FA and ADC values and histopathological subtype of the primary tumor was investigated.

RESULTS

The mean ADC and FA values obtained from the solid part of the brain metastases of SCLC were significantly lower than the NSCLC metastases (P < 0.001 and P = 0.003, respectively). ROC curve analysis showed diagnostic performance for mean ADC values (AUC=0.889, P = < 0.001) and FA values (AUC = 0.677, P = 0.002). Cut-off value of > 0.909 × 10^-3 mm²/s for mean ADC (Sensitivity = 80.3, Specificity = 83.8, PPV = 89.1, NPV = 72.1) and > 0.139 for FA values (Sensitivity = 80.3, Specificity = 54.1, PPV = 74.2, NPV= 62.5) revealed in differentiating NSCLC from NSCLC.

CONCLUSION

DTI parameters of brain metastasis can discriminate SCLC and NSCLC. ADC and FA values of metastatic brain lesions due to the lung cancer may be an important tool to differentiate histopathological subgroups. DTI may guide clinicians for the management of intracranial metastatic lesions of lung cancer.

Reduced fractional anisotropy of the genu of the corpus callosum as a cerebrovascular disease marker and predictor of longitudinal cognition in MCI

Our goal was to evaluate the utility of diffusion tensor imaging (DTI) for predicting future cognitive decline in mild cognitive impairment (MCI) in conjunction with Alzheimer's disease (AD) biomarkers (amyloid positron emission tomography and AD signature neurodegeneration) in 132 MCI individuals ≥60 year old with structural magnetic resonance imaging, DTI, amyloid positron emission tomography, and at least one clinical follow-up. We used mixed-effect models to evaluate the prognostic ability of fractional anisotropy of the genu of the corpus callosum (FA-Genu), as a cerebrovascular disease marker, for predicting cognitive decline along with AD biomarkers. We contrasted the value of white matter hyperintensities, a traditional cerebrovascular disease marker as well as FA in the hippocampal cingulum bundle with the FA-Genu models. FA-Genu significantly predicted cognitive decline even after accounting for AD biomarkers. WMH was not associated with cognitive decline in the model with both WMH and FA-Genu. DTI specifically FA-Genu provides unique complementary information to AD biomarkers and has significant utility for prediction of cognitive decline in MCI.

Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer's Disease

Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer's disease (AD). Several scores such as Alzheimer's Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7).

Correlation Between DTI Findings and Volume of Corpus Callosum in Children with AUTISM

BACKGROUND

Autism Spectrum Disorder (ASD) is a complex developmental disorder in which neurological basis is largely unknown. The Corpus Callosum (CC) is the main commissure that connects the cerebral hemispheres. Previous evidence suggests the involvement of the CC in the pathophysiology of autism.

AIM

The aim of our study is to assess whether there were any changes in Corpus Callosum (CC) area and volume and to reveal the relationship between Diffusion Tensor Imaging (DTI) features in genu and splenium of corpus callosum in children with ASD.

METHODS

Eighteen patient and 15 controls were recruited. The volumetric sagittal TI images were used to provide measurements of midsagittal corpus callosum surface area while FA, MD, RD, and ADC values were extracted from genu and splenium of corpus callosum after which the correlation in the area and volume in ASD children was examined.

RESULTS

CC area and volume in children with ASD were decreased than controls. FA values obtained from the genu and splenum of CC were significantly lower and RD values were significantly higher. A positive correlation was observed between the FA of the genu and splenium and area and volume of the CC. There was a negative correlation between ADC, MD and RD of CC and area and volume measurements.

CONCLUSION

The conclusions in the interrelations of morphometric and DTI data may demonstrate a likelihood of damages in the axons and cortical neurons. The results showed that there existed microstructural damages from the DTI findings. Furthermore, the decrease in FA could be a representation of the reduction in the myelination in nerve pathways, impaired integrity, reduced axonal density, and organization. Indeed, the changes in volumetric and microstructural of CC could be useful in evaluating underlying pathophysiology in children with autism.

Cortical atrophy patterns of incident MCI subtypes in the Mayo Clinic Study of Aging

INTRODUCTION

We examined differences in cortical thickness in empirically derived mild cognitive impairment (MCI) subtypes in the Mayo Clinic Study of Aging.

METHODS

We compared cortical thickness of four incident MCI subtypes (n = 192) to 1257 cognitive unimpaired individuals.

RESULTS

The subtle cognitive impairment cluster had atrophy in the entorhinal and parahippocampal cortex. The amnestic, dysnomic, and dysexecutive clusters also demonstrated entorhinal cortex atrophy as well as thinning in temporal, parietal, and frontal isocortex in somewhat different patterns.

DISCUSSION

We found patterns of atrophy in each of the incident MCI clusters that corresponded to their patterns of cognitive impairment. The identification of MCI subtypes based on cognitive and structural features may allow for more efficient trial and study designs. Given individuals in the subtle cognitive impairment cluster have less structural changes and cognitive decline and may represent the earliest group, this could be a unique group to target with early interventions.

Effects of an APOE Promoter Polymorphism on Fronto-Parietal Functional Connectivity During Nondemented Aging

Background: The rs405509 polymorphism ofthe apolipoprotein E (APOE) promoter is related to Alzheimer'sdisease (AD). The T/T allele of rs405509 is known to decrease the transcription of the APOE gene and lead to impairments in specific brain structural networks with aging; thus, it is an important risk factor for AD. However, it remains unknown whether rs405509 affects brain functional connectivity (FC) in aging. Methods: We investigated the effect of the rs405509 genotype (T/T vs. G-allele) on age-related brain FC using functional magnetic resonance imaging. Forty-five elderly TT carriers and 45 elderly G-allele carriers were scanned during a working memory (WM) task. Results: We found that TT carriers showed an accelerated age-related increase in functional activation in the left postcentral gyrus compared with G-allele carriers. Furthermore, the FC between the left postcentral gyrus and some key regions during WM performance, including the right caudal and superior frontal sulcus (SFS), was differentially modulated by age across rs405509 genotype groups. Conclusions: These results demonstrate that the rs405509 T/T allele of APOE causes an age-related brain functional decline in nondemented elderly people, which may be beneficial for understanding the neural mechanisms of rs405509-related cognitive aging and AD pathogenesis.

Assessment of Auditory Pathways Using Diffusion Tensor Imaging in Patients with Neurofibromatosis Type 1

Aim:

The aim of our study was to determine whether the diffusion properties of the auditory pathways alter between patients with Neurofibromatosis type 1 (NF1) and the healthy subjects. DTI can well demonstrate FA and ADC changes in auditory tracts and it may be a guide to identify the candidates for hearing loss among NF1 children.

Methods:

The study population consisted of 43 patients with NF1 and 21 healthy controls. Diffusion tensor imaging (DTI) was used to measure apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values from lemniscus lateralis, colliculus inferior, corpus geniculatum mediale and Heschl's gyrus. The results were compared with those of the control group.

Results:

The ADC values of lateral lemniscus, colliculus inferior and corpus geniculatum mediale were significantly higher in NF1 compared to those of the control group. On the other hand, decreased FA values were observed in lateral lemniscus and colliculus inferior in patients with NF1.

Conclusion:

The increase in ADC and reduction in FA in the auditory pathways of patients with NF1 may suggest microstructural alterations, such as a decrease in the number of axons, edema or inflammation in the auditory tracts.

The Role of MRI Biomarkers and Their Interactions with Cognitive Status and APOE ε4 in Nondemented Elderly Subjects

PURPOSE

(1) To investigate atrophy patterns of hippocampal subfield volume and Alzheimer's disease (AD)-signature cortical thickness in mild cognitive impairment (MCI) patients; (2) to explore the association between the neuropsychological (NP) and the brain structure in the MCI and older normal cognition group; (3) to determine whether these associations were modified by the apolipoprotein E (APOE) ε4 gene and cognitive status.

METHODS

The FreeSurfer software was used for automated segmentation of hippocampal subfields and AD-signature cortical thickness for 22 MCI patients and 23 cognitive normal controls (NC). The volume, cortical thickness, and the neuropsychological scale were compared with two-sample t tests. Linear regression models were used to determine the association between the NP and the brain structure.

RESULTS

Compared with the NC group, MCI patients showed a decreased volume of the left presubiculum, subiculum and right CA2_3 and CA4_DG (p < 0.05, FDR corrected). The volume of these regions was positively correlated with NP scores. Of note, these associations depended on the cognitive status but not on the APOE ε4 status. The left subiculum and presubiculum volume were positively correlated with the Montreal Cognitive Assessment (MoCA) scores only in the MCI patients.

CONCLUSION

Atrophy of the hippocampal subfields may be a powerful biomarker for MCI in the Chinese population.

Differentiating between Alzheimer's disease, amnestic mild cognitive impairment, and normal aging via diffusion kurtosis imaging

Diffusion kurtosis imaging can be used to assess pathophysiological changes in tissue structure and to diagnose central nervous system diseases. However, its sensitivity in assessing hippocampal differences between patients with Alzheimer's disease and those with amnestic mild cognitive impairment has not been characterized. Here, we examined 20 individuals with Alzheimer's disease (11 men and 9 women, mean 73.2 ± 4.49 years), 20 with amnestic mild cognitive impairment (10 men and 10 women, mean 71.55 ± 4.77 years), and 20 normal controls (11 men and 9 women, mean 70.45 ± 5.04 years). We conducted diffusion kurtosis imaging, using a 3.0 T magnetic resonance scanner, to compare hippocampal differences among the three groups. The results demonstrated that the right hippocampal volume and bilateral mean kurtosis were remarkably smaller in individuals with Alzheimer's disease compared with those with amnestic mild cognitive impairment and normal controls. Further, the mean kurtosis was lower in the amnestic mild cognitive impairment group compared with the normal control group. The mean diffusion in the left hippocampus was lower in the Alzheimer's disease group than in the amnestic mild cognitive impairment and normal control groups, while the mean diffusion in the right hippocampus was lower in the Alzheimer's disease group than in the normal control group. Fractional anisotropy was similar among the three groups. These results verify that bilateral mean kurtosis and mean diffusion are sensitive to the diagnosis of Alzheimer's disease and amnestic mild cognitive impairment. This study was approved by the Ethics Review Board of Affiliated Sixth People's Hospital of Shanghai Jiao Tong University, China on May 4, 2010 (approval No. 2010(C)-6).

Interpretable High-Dimensional Inference Via Score Projection with an Application in Neuroimaging

In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the associations between the outcome and summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with the outcome. Here, we propose a generalization of Rao's score test based on projecting the score statistic onto a linear subspace of a high-dimensional parameter space. The approach provides a way to localize signal in the high-dimensional space by projecting the scores to the subspace where the score test was performed. This allows for inference in the high-dimensional space to be performed on the same degrees of freedom as the score test, effectively reducing the number of comparisons. Simulation results demonstrate the test has competitive power relative to others commonly used. We illustrate the method by analyzing a subset of the Alzheimer's Disease Neuroimaging Initiative dataset. Results suggest cortical thinning of the frontal and temporal lobes may be a useful biological marker of Alzheimer's disease risk.

Identification of Alzheimer's Disease and Mild Cognitive Impairment Using Networks Constructed Based on Multiple Morphological Brain Features

Structural brain markers are important for characterizing the pathology of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Here, we constructed a multifeature-based network (MFN) for each individual using a sparse linear regression performed on six types of morphological features to promote the structure-based autodiagnosis. The categorization performance of the MFN was evaluated in 165 normal control subjects, 221 patients with MCI, and 142 patients with AD. We achieved 96.42% and 96.37% accuracy, respectively, in distinguishing the patients with AD and MCI from the normal control subjects, and reasonable discrimination of the two disease cohorts (70.52%) and prediction of the MCI to AD progression (65.61%). The performance was further improved by combining the properties of the MFN with the morphological features. Our results demonstrate the effectiveness of the MFN in combination with morphological features obtained from single imaging modality, serving as robust biomarkers in the diagnosis of AD and MCI.

Diagnostic Validity of an Automated Probabilistic Tractography in Amnestic Mild Cognitive Impairment

Objective

Although several prior works showed the white matter (WM) integrity changes in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease, it is still unclear the diagnostic accuracy of the WM integrity measurements using diffusion tensor imaging (DTI) in discriminating aMCI from normal controls. The aim of this study is to explore diagnostic validity of whole brain automated probabilistic tractography in discriminating aMCI from normal controls.

Methods

One hundred-two subjects (50 aMCI and 52 normal controls) were included and underwent DTI scans. Whole brain WM tracts were reconstructed with automated probabilistic tractography. Fractional anisotropy (FA) and mean diffusivity (MD) values of the memory related WM tracts were measured and compared between the aMCI and the normal control groups. In addition, the diagnostic validities of these WM tracts were evaluated.

Results

Decreased FA and increased MD values of memory related WM tracts were observed in the aMCI group compared with the control group. Among FA and MD value of each tract, the FA value of left cingulum angular bundle showed the highest area under the curve (AUC) of 0.85 with a sensitivity of 88.2%, a specificity of 76.9% in differentiating MCI patients from control subjects. Furthermore, the combination FA values of WM integrity measures of memory related WM tracts showed AUC value of 0.98, a sensitivity of 96%, a specificity of 94.2%.

Conclusion

Our results with good diagnostic validity of WM integrity measurements suggest DTI might be promising neuroimaging tool for early detection of aMCI and AD patients.

What can DTI tell about early cognitive impairment? - Differentiation between MCI subtypes and healthy controls by diffusion tensor imaging

Mild cognitive impairment (MCI) gained a lot of interest recently, especially that the conversion rate to Alzheimer Disease (AD) in the amnestic subtype (aMCI) is higher than in the non-amnestic subtype (naMCI). We aimed to determine whether and how diffusion-weighted MRI (DWI) using the diffusion tensor model (DTI) can differentiate MCI subtypes from healthy subjects. High resolution 3D T1W and DWI images of patients (aMCI, n = 18; naMCI, n = 20; according to Petersen criteria) and controls (n = 27) were acquired at 3T and processed using ExploreDTI and SPM. Voxel-wise and region of interest (ROI) analyses of fractional anisotropy (FA) and mean diffusivity (MD) were performed with ANCOVA; MD was higher in aMCI compared to controls or naMCI in several grey and white matter (GM, WM) regions (especially in the temporal pole and the inferior temporal lobes), while FA was lower in WM ROI-s (e.g. left Cingulum). Moreover, significant correlations were identified between verbal fluency, visual and verbal memory performance and DTI metrics. Logistic regression showed that measuring FA of the crus of fornix along GM volumetry improves the discrimination of aMCI from naMCI. Additional information from DWI/DTI aids preclinical detection of AD and may help detecting early non-Alzheimer type dementia, too.

Working Memory And Brain Tissue Microstructure: White Matter Tract Integrity Based On Multi-Shell Diffusion MRI

Working memory is a complex cognitive process at the intersection of sensory processing, learning, and short-term memory and also has a general executive attention component. Impaired working memory is associated with a range of neurological and psychiatric disorders, but very little is known about how working memory relates to underlying white matter (WM) microstructure. In this study, we investigate the association between WM microstructure and performance on working memory tasks in healthy adults (right-handed, native English speakers). We combine compartment specific WM tract integrity (WMTI) metrics derived from multi-shell diffusion MRI as well as diffusion tensor/kurtosis imaging (DTI/DKI) metrics with Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests tapping auditory working memory. WMTI is a novel tool that helps us describe the microstructural characteristics in both the intra- and extra-axonal environments of WM such as axonal water fraction (AWF), intra-axonal diffusivity, extra-axonal axial and radial diffusivities, allowing a more biophysical interpretation of WM changes. We demonstrate significant positive correlations between AWF and letter-number sequencing (LNS), suggesting that higher AWF with better performance on complex, more demanding auditory working memory tasks goes along with greater axonal volume and greater myelination in specific regions, causing efficient and faster information process.

The Added Value of Diffusion-Weighted MRI-Derived Structural Connectome in Evaluating Mild Cognitive Impairment: A Multi-Cohort Validation1

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.

Neonatal Hypoxia Ischaemia: Mechanisms, Models, and Therapeutic Challenges

Neonatal hypoxia-ischaemia (HI) is the most common cause of death and disability in human neonates, and is often associated with persistent motor, sensory, and cognitive impairment. Improved intensive care technology has increased survival without preventing neurological disorder, increasing morbidity throughout the adult population. Early preventative or neuroprotective interventions have the potential to rescue brain development in neonates, yet only one therapeutic intervention is currently licensed for use in developed countries. Recent investigations of the transient cortical layer known as subplate, especially regarding subplate's secretory role, opens up a novel set of potential molecular modulators of neonatal HI injury. This review examines the biological mechanisms of human neonatal HI, discusses evidence for the relevance of subplate-secreted molecules to this condition, and evaluates available animal models. Neuroserpin, a neuronally released neuroprotective factor, is discussed as a case study for developing new potential pharmacological interventions for use post-ischaemic injury.

White-matter integrity on DTI and the pathologic staging of Alzheimer's disease

Pattern of diffusion tensor MRI (DTI) alterations were investigated in pathologically-staged Alzheimer's disease (AD) patients (n = 46). Patients with antemortem DTI studies and a range of AD pathology at autopsy were included. Patients with a high neurofibrillary tangle (NFT) stage (Braak IV-VI) had significantly elevated mean diffusivity (MD) in the crus of fornix and ventral cingulum tracts, precuneus, and entorhinal white matter on voxel-based analysis after adjusting for age and time from MRI to death (p < 0.001). Higher MD and lower fractional anisotropy in the ventral cingulum tract, entorhinal, and precuneus white matter was associated with higher Braak NFT stage and clinical disease severity. There were no MD and fractional anisotropy differences among the low (none and sparse) and high (moderate and frequent) β-amyloid neuritic plaque groups. The NFT pathology of AD is associated with DTI alterations involving the medial temporal limbic connections and medial parietal white matter. This pattern of diffusion abnormalities is also associated with clinical disease severity.

Parallel Atrophy of Cortex and Basal Forebrain Cholinergic System in Mild Cognitive Impairment

The basal forebrain cholinergic system (BFCS) is the major source of acetylcholine for the cerebral cortex in humans. The aim was to analyze the pattern of BFCS and cortical atrophy in MCI patients to find evidence for a parallel atrophy along corticotopic organization of BFCS projections. BFCS volume and cortical thickness were analyzed using high-definition 3D structural magnetic resonance imaging data from 1.5-T and 3.0-T scanners of 64 MCI individuals and 62 cognitively healthy elderly controls from the European DTI study in dementia. BFCS volume reduction was correlated with thinning of cortical areas with known BFCS projections, such as Ch2 and parahippocampal gyrus in the MCI group, but not in the control group. Additionally, we found correlations between BFCS and cortex atrophy beyond the known corticotopic projections, such as between Ch4p and the cingulate gyrus. BFCS volume reduction was associated with regional thinning of cortical areas that included, but was not restricted to, the pattern of corticotopic projections of the BFCS as derived from animal studies. Our in vivo results may indicate the existence of more extended projections from the BFCS to the cerebral cortex in humans than that known from prior studies with animals.

Mild Cognitive Impairments Moderate the Effect of Time on Verbal Fluency Performance

OBJECTIVES

Mild cognitive impairments (MCI) is a transitional state in aging associated with increased risk of incident dementia. The current study investigated whether MCI status moderated the effect of time on word generation during verbal fluency tasks. Specifically, the objective was to determine whether MCI status had differential effects on initial automatic or latter more effortful retrieval processes of fluency tasks.

METHODS

Participants were community residing older adults enrolled in a longitudinal cohort study. Of the 408 participants, 353 were normal (age=76.06±6.61; %female=57.8) and 55 were diagnosed with MCI (age=78.62±7.00; %female=52.7). Phonemic and category fluency were each administered for 60 s, but performance was recorded at three consecutive 20-s intervals (0-20 s [T1], 21-40 s [T2], 41-60 s [T3]. Separate linear mixed effects models for each fluency task were used to determine the effects of group, time, and their interaction on word generation.

RESULTS

In both fluency tasks, word generation declined as a function of time. Individuals with MCI generated fewer words compared to controls during the first 20 s of phonemic (beta=-1.56; p<.001; d=0.28) and category fluency (beta=-1.85; p<.001; d=0.37). Group by time interactions revealed that individuals with MCI demonstrated attenuated declines in word generation from the first to the second and third time intervals of both phonemic ([T1 vs. T2] beta=2.17, p=.001; d=0.41; [T1 vs. T3]beta=2.28, p=.001; d=0.45) and category ([T1 vs. T2] beta= 2.22, p=.002; d=0.50; [T1 vs. T3]beta=3.16, p<.001; d=0.71) fluency.

CONCLUSIONS

Early automatic retrieval processes in verbal fluency tasks are compromised in MCI. (JINS, 2017, 23, 44-55).

Small world properties changes in mild traumatic brain injury

PURPOSE

To investigate local and global efficiency changes characterized by small-world properties based on resting-state functional MRI, such as centrality and clustering coefficient, in mild traumatic brain injury (MTBI) patients; and to associate these findings with axonal injury as measured by diffusion tensor imaging (DTI) as well as with post-concussive symptom (PCS).

MATERIALS AND METHODS

Thirty patients (mean age 35 ± 13 years) with clinically defined MTBI and 45 age-matched healthy controls (mean age 37 ± 10 years) participated in the experiments. Resting-state functional MRI was performed using gradient echo planar imaging sequence with 3 Tesla MRI scanner to obtain functional small-world networks. Out of all participants, 20 MTBI patients and 20 controls had available DTI data with three b-values (0, 500, 1000) s/mm² and 30 directions for diffuse axonal injury analyses.

RESULTS

Compared with controls, MTBI patients showed lower relative betweenness centrality (P = 0.01), but significantly higher clustering coefficient (P = 0.04), and these two metrics correlated negatively in patients (r = -0.77; P < 0.001). Regions with lower betweenness centrality (e.g., frontal and occipital) corresponded with the regions of reduced FA in patients, while global FA reduction correlated with betweenness centrality (r = 0.48; P = 0.03) and clustering coefficient (r = -0.46; P = 0.04) in MTBI patients. In addition, there was significantly higher thalamocortical connectivity that correlated with clustering coefficient (r = 0.39; P = 0.03) in patients. Also, patients with higher clustering coefficient tended to have less PCS score with negative correlation (r = -0.4; P = 0.04).

CONCLUSION

Our results demonstrated significant functional small-world properties changes in patients with MTBI, and suggest decreased global efficiency, possibly due to diffuse axonal injury and local network upregulation including increased thalamo-cortical connectivity.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:518-527.

Heterogeneous cortical atrophy patterns in MCI not captured by conventional diagnostic criteria

OBJECTIVE

We investigated differences in regional cortical thickness between previously identified empirically derived mild cognitive impairment (MCI) subtypes (amnestic MCI, dysnomic MCI, dysexecutive/mixed MCI, and cluster-derived normal) in order to determine whether these cognitive subtypes would show different patterns of cortical atrophy.

METHODS

Participants were 485 individuals diagnosed with MCI and 178 cognitively normal individuals from the Alzheimer's Disease Neuroimaging Initiative. Cortical thickness estimates were computed for 32 regions of interest per hemisphere. Statistical group maps compared each MCI subtype to cognitively normal participants and to one another.

RESULTS

The pattern of cortical thinning observed in each MCI subtype corresponded to their cognitive profile. No differences in cortical thickness were found between the cluster-derived normal MCI subtype and the cognitively normal group. Direct comparison between MCI subtypes suggested that the cortical thickness patterns reflect increasing disease severity.

CONCLUSIONS

There is an ordered pattern of cortical atrophy among patients with MCI that coincides with their profiles of increasing cognitive dysfunction. This heterogeneity is not captured when patients are grouped by conventional diagnostic criteria. Results in the cluster-derived normal group further support the premise that the conventional MCI diagnostic criteria are highly susceptible to false-positive diagnostic errors. Findings suggest a need to (1) improve the diagnostic criteria by reducing reliance on conventional screening measures, rating scales, and a single memory measure in order to avoid false-positive errors; and (2) divide MCI samples into meaningful subgroups based on cognitive and biomarkers profiles-a method that may provide better staging of MCI and inform prognosis.

Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood

Mapping aging-related brain structure and connectivity changes can be helpful for assessing physiological brain age (PBA), which is distinct from chronological age (CA) because genetic and environmental factors affect individuals differently. This study proposes an approach whereby structural and connectomic information can be combined to estimate PBA as an early biomarker of brain aging. In a cohort of 136 healthy adults, magnetic resonance and diffusion tensor imaging are respectively used to measure cortical thickness over the entire cortical mantle as well as connectivity properties (mean connectivity density and mean fractional anisotropy) for white matter connections. Using multivariate regression, these measurements are then employed to (1) illustrate how CA can be predicated--and thereby also how PBA can be estimated--and to conclude that (2) healthy aging is associated with significant connectome changes during adulthood. Our study illustrates a connectomically-informed statistical approach to PBA estimation, with potential applicability to the clinical identification of patients who exhibit accelerated brain aging, and who are consequently at higher risk for developing mild cognitive impairment or dementia.

Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer's Disease

Brain network occupies an important position in representing abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Currently, most studies only focused on morphological features of regions of interest without exploring the interregional alterations. In order to investigate the potential discriminative power of a morphological network in AD diagnosis and to provide supportive evidence on the feasibility of an individual structural network study, we propose a novel approach of extracting the correlative features from magnetic resonance imaging, which consists of a two-step approach for constructing an individual thickness network with low computational complexity. Firstly, multi-distance combination is utilized for accurate evaluation of between-region dissimilarity; and then the dissimilarity is transformed to connectivity via calculation of correlation function. An evaluation of the proposed approach has been conducted with 189 normal controls, 198 MCI subjects, and 163 AD patients using machine learning techniques. Results show that the observed correlative feature suggests significant promotion in classification performance compared with cortical thickness, with accuracy of 89.88% and area of 0.9588 under receiver operating characteristic curve. We further improved the performance by integrating both thickness and apolipoprotein E ɛ4 allele information with correlative features. New achieved accuracies are 92.11% and 79.37% in separating AD from normal controls and AD converters from non-converters, respectively. Differences between using diverse distance measurements and various correlation transformation functions are also discussed to explore an optimal way for network establishment.