Comprehensive dissection of the medial temporal lobe in AD: measurement of hippocampus, amygdala, entorhinal, perirhinal and parahippocampal cortices using MRI

Unraveling the complexity of human brain: Structure, function in healthy and disease states

The human brain stands as an intricate organ, embodying a nexus of structure, function, development, and diversity. This review delves into the multifaceted landscape of the brain, spanning its anatomical intricacies, diverse functional capacities, dynamic developmental trajectories, and inherent variability across individuals. The dynamic process of brain development, from early embryonic stages to adulthood, highlights the nuanced changes that occur throughout the lifespan. The brain, a remarkably complex organ, is composed of various anatomical regions, each contributing uniquely to its overall functionality. Through an exploration of neuroanatomy, neurophysiology, and electrophysiology, this review elucidates how different brain structures interact to support a wide array of cognitive processes, sensory perception, motor control, and emotional regulation. Moreover, it addresses the impact of age, sex, and ethnic background on brain structure and function, and gender differences profoundly influence the onset, progression, and manifestation of brain disorders shaped by genetic, hormonal, environmental, and social factors. Delving into the complexities of the human brain, it investigates how variations in anatomical configuration correspond to diverse functional capacities across individuals. Furthermore, it examines the impact of neurodegenerative diseases on the structural and functional integrity of the brain. Specifically, our article explores the pathological processes underlying neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, shedding light on the structural alterations and functional impairments that accompany these conditions. We will also explore the current research trends in neurodegenerative diseases and identify the existing gaps in the literature. Overall, this article deepens our understanding of the fundamental principles governing brain structure and function and paves the way for a deeper understanding of individual differences and tailored approaches in neuroscience and clinical practice-additionally, a comprehensive understanding of structural and functional changes that manifest in neurodegenerative diseases.

The current status and challenges of olfactory dysfunction study in Alzheimer's Disease

Olfactory functioning involves multiple cognitive processes and the coordinated actions of various neural systems. Any disruption at any stage of this process may result in olfactory dysfunction, which is consequently widely used to predict the onset and progression of diseases, such as Alzheimer's Disease (AD). Although the underlying mechanisms have not yet been fully unraveled, apparent changes were observed in olfactory brain areas form patients who suffer from AD by means of medical imaging and electroencephalography (EEG). Olfactory dysfunction holds significant promise in detecting AD during the preclinical stage preceding mild cognitive impairment (MCI). Owing to the strong specificity, olfactory tests are prevalently applied for screening in community cohorts. And combining olfactory tests with other biomarkers may further establish an optimal model for AD prediction in studies of specific olfactory dysfunctions and improve the sensitivity and specificity of early AD diagnosis.

Occlusion dysfunction and Alzheimer's disease: Mendelian randomization study

Aim

Occlusion dysfunction (OD) is increasingly linked to Alzheimer's disease (AD). This study aimed to elucidate the causal relationship between OD and AD using Mendelian randomization (MR) analysis.

Materials and methods

Genome-wide association study (GWAS) meta-analysis data obtained from FinnGen, IEU Open GWAS, and UK Biobank (UKBB) was represented as instrumental variables. We validated the causal relationship between periodontal disease (PD), loose teeth (PD & occlusion dysfunction), dentures restoration (occlusion recovery), and AD.

Results

According to the MR analysis, PD and AD have no direct causal relationship (P = 0.395, IVW). However, loose teeth significantly increased the risk of AD progression (P = 0.017, IVW, OR = 187.3567, 95%CI = 2.54E+00-1.38E+04). These findings were further supported by the negative causal relationship between dentures restoration and AD (P = 0.015, IVW, OR = 0.0234, 95%CI = 1.13E-03-0.485).

Conclusion

The occlusion dysfunction can ultimately induce Alzheimer's disease. Occlusion function was a potentially protective factor for maintaining neurological health.

Neurophysiological trajectories in Alzheimer's disease progression

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Neurophysiological trajectories in Alzheimer's disease progression

Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

Preliminary study on early diagnosis of Alzheimer's disease in APP/PS1 transgenic mice using multimodal magnetic resonance imaging

Alzheimer's disease (AD) has an insidious onset and lacks clear early diagnostic markers, and by the time overt dementia symptoms appear, the disease is already in the mid-to-late stages. The search for early diagnostic markers of AD may open a critical window for Alzheimer's treatment and facilitate early intervention to slow the progression of AD. In this study, we aimed to explore the imaging markers for early diagnosis of AD through the combined application of structural magnetic resonance imaging (sMRI), resting-state functional magnetic resonance imaging (rs-fMRI), and 1H-magnetic resonance spectroscopy (1H-MRS) multimodal magnetic resonance imaging (MRI) techniques at the animal experimental level, with the aim to provide a certain reference for early clinical diagnosis of AD. First, sMRI scans were performed on 4-month-old amyloid beta precursor protein/presenilin 1 (APP/PS1) transgenic AD model mice and wild type mice of the same litter using a 7.0 T animal MRI scanner to analyze the differential brain regions with structural changes in the gray matter of the brain by voxel-based morphometry (VBM). Next, rs-fMRI scans were performed to analyze the differential brain regions between groups for local spontaneous brain activity and functional connectivity (FC) between brain regions. Finally, 1H-MRS scans were performed to quantify and analyze intergroup differences in the relative concentrations of different metabolites within regions of interest (cortex and hippocampus). Compared with wild type mice, the volume of the left hippocampus, and right olfactory bulb of APP/PS1 transgenic AD model mice were reduced, the functional activity of the bilateral hippocampus, right piriform cortex and right caudate putamen was reduced, the functional network connectivity of the hippocampus was impaired, and the relative content of N-acetylaspartate (NAA)in the hippocampus was decreased. In addition, this study found that imaging changes in olfactory-related brain regions were closely associated with AD diagnosis, and these findings may provide some reference for the early diagnosis of AD.

Changes in Multiparametric Magnetic Resonance Imaging and Plasma Amyloid-Beta Protein in Subjective Cognitive Decline

The association between plasma amyloid-beta protein (Aβ) and subjective cognitive decline (SCD) remains controversial. We aimed to explore the correlation between neuroimaging findings, plasma Aβ, and neuropsychological scales using data from 53 SCD patients and 46 age- and sex-matched healthy controls (HCs). Magnetic resonance imaging (MRI) was used to obtain neuroimaging data for a whole-brain voxel-based morphometry analysis and cortical functional network topological features. The SCD group had slightly lower Montreal Cognitive Assessment (MoCA) scores than the HC group. The Aβ42 levels were significantly higher in the SCD group than in the HC group (p < 0.05). The SCD patients demonstrated reduced volumes in the left hippocampus, right rectal gyrus (REC.R), and right precentral gyrus (PreCG.R); an increased percentage fluctuation in the left thalamus (PerAF); and lower average small-world coefficient (aSigma) and average global efficiency (aEg) values. Correlation analyses with Aβ and neuropsychological scales revealed significant positive correlations between the volumes of the HIP.L, REC.R, PreCG.R, and MoCA scores. The HIP.L volume and Aβ42 were negatively correlated, as were the REC.R volume and Aβ42/40. PerAF and aSigma were negatively and positively correlated with the MoCA scores, respectively. The aEg was positively correlated with Aβ42/40. SCD patients may exhibit alterations in plasma biomarkers and multi-parameter MRI that resemble those observed in Alzheimer's disease, offering a theoretical foundation for early clinical intervention in SCD.

MRI measurements of brain hippocampus volume in relation to mild cognitive impairment and Alzheimer disease: A systematic review and meta-analysis

BACKGROUND

This is the first meta-analysis conducted to compare the hippocampal volume measured by magnetic resonance imaging (MRI) in healthy normal subjects, mild cognitive impairment (MCI) and Alzheimer disease (AD), and to analyze the relationship between hippocampal volume changes and MCI and AD.

METHODS

English literatures published from January 2004 to December 2006 were extracted from PubMed, Embase, Wanfang Medical, and China National Knowledge Infrastructure databases. Statistical analysis was carried out with Stata/SE 16.0 software.

RESULTS

The smaller the volume of the hippocampus measured by MRI, the more severe the cognitive impairment or AD. Different MRI post-measurement correction methods have different measurement results: Left hippocampal volume measured by MRI Raw volume method is negatively correlated with MCI and AD (OR [odds ratio] = 0.58, 95%CI [confidence interval]: 0.42, 0.75) right hippocampal volume measured was not associated with MCI OR AD (OR = 0.87, 95%CI: 0.56, 1.18); left hippocampal volume measured by MRI total intracranial volume (TIV) Correction was not associated with MCI and AD (OR = 0.90, 95%CI: 0.62, 1.19), measured right hippocampal volume was not associated with MCI OR AD (OR = 0.81, 95%CI: 0.49, 1.12); left hippocampal volume measured by MRI TIV Correction was not associated with MCI and AD (OR = 0.90, 95%CI: 0.62, 1.19), measured right hippocampus volume was negatively associated with MCI and AD (OR = 0.49, 95%CI: 0.35, 0.62).

CONCLUSION

The shrinkage of hippocampus volume is closely related to MCI and AD. MRI measurement of hippocampus volume is not only an auxiliary diagnostic tool for MCI and AD, but also a good prognosis assessment tool.

Multi-omic atlas of the parahippocampal gyrus in Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia worldwide, with a projection of 151 million cases by 2050. Previous genetic studies have identified three main genes associated with early-onset familial Alzheimer's disease, however this subtype accounts for less than 5% of total cases. Next-generation sequencing has been well established and holds great promise to assist in the development of novel therapeutics as well as biomarkers to prevent or slow the progression of this devastating disease. Here we present a public resource of functional genomic data from the parahippocampal gyrus of 201 postmortem control, mild cognitively impaired (MCI) and AD individuals from the Mount Sinai brain bank, of which whole-genome sequencing (WGS), and bulk RNA sequencing (RNA-seq) were previously published. The genomic data include bulk proteomics and DNA methylation, as well as cell-type-specific RNA-seq and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) data. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource available on the Synapse platform at https://doi.org/10.7303/syn51180043.2 .

Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors

Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.

Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity

Neuroimaging‐driven brain age estimation has become popular in measuring brain aging and identifying neurodegenerations. However, the single estimated brain age (gap) compromises regional variations of brain aging, losing spatial specificity across diseases which is valuable for early screening. In this study, we combined brain age modeling with Shapley Additive Explanations to measure brain aging as a feature contribution vector underlying spatial pathological aging mechanism. Specifically, we regressed age with volumetric brain features using machine learning to construct the brain age model, and model‐agnostic Shapley values were calculated to attribute regional brain aging for each subject's age estimation, forming the brain age vector. Spatial specificity of the brain age vector was evaluated among groups of normal aging, prodromal Parkinson disease (PD), stable mild cognitive impairment (sMCI), and progressive mild cognitive impairment (pMCI). Machine learning methods were adopted to examine the discriminability of the brain age vector in early disease screening, compared with the other two brain aging metrics (single brain age gap, regional brain age gaps) and brain volumes. Results showed that the proposed brain age vector accurately reflected disorder‐specific abnormal aging patterns related to the medial temporal and the striatum for prodromal AD (sMCI vs. pMCI) and PD (healthy controls [HC] vs. prodromal PD), respectively, and demonstrated outstanding performance in early disease screening, with area under the curves of 83.39% and 72.28% in detecting pMCI and prodromal PD, respectively. In conclusion, the proposed brain age vector effectively improves spatial specificity of brain aging measurement and enables individual screening of neurodegenerative diseases.

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Background

Functional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model.

Method

Functional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model.

Results

Real EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups.

Conclusion

Taken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.

Hippocampo-Horn Percentage and Parietal Atrophy Score for Easy Visual Assessment of Brain Atrophy on Magnetic Resonance Imaging in Early- and Late-Onset Alzheimer's Disease

BACKGROUND

Magnetic resonance imaging (MRI) visual scales of brain atrophy are important for differential diagnosis of dementias in routine clinical practice. Atrophy patterns in early- and late-onset Alzheimer's disease (AD) can be different according to some studies.

OBJECTIVE

Our goal was to assess brain atrophy patterns in early- and late-onset AD using our recently developed simple MRI visual scales and evaluate their reliability.

METHODS

We used Hippocampo-horn percentage (Hip-hop) and Parietal Atrophy Score (PAS) to compare mediotemporal and parietal atrophy on brain MRI among 4 groups: 26 patients with early-onset AD, 21 younger cognitively normal persons, 32 patients with late-onset AD, and 36 older cognitively normal persons. Two raters scored all brain MRI to assess reliability of the Hip-hop and PAS. Brain MRIs were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

RESULTS

The patients with early-onset AD had significantly more pronounced mediotemporal and also parietal atrophy bilaterally compared to the controls (both p < 0.01). The patients with late-onset AD had significantly more pronounced only mediotemporal atrophy bilaterally compared to the controls (p < 0.000001), but parietal lobes were the same. Intra-rater and inter-rater reliability of both visual scales Hip-hop and PAS were almost perfect in all cases (weighted-kappa value ranged from 0.90 to 0.99).

CONCLUSION

While mediotemporal atrophy detected using Hip-hop is universal across the whole AD age spectrum, parietal atrophy detected using PAS is worth rating only in early-onset AD. Hip-hop and PAS are very reliable MRI visual scales.

Cortical structure and the risk for Alzheimer's disease: a bidirectional Mendelian randomization study

Progressive loss of neurons in a specific brain area is one of the manifestations of Alzheimer's disease (AD). Much effort has been devoted to investigating brain atrophy and AD. However, the causal relationship between cortical structure and AD is not clear. We conducted a bidirectional two-sample Mendelian randomization analysis to investigate the causal relationship between cortical structure (surface area and thickness of the whole cortex and 34 cortical regions) and AD risk. Genetic variants used as instruments came from a large genome-wide association meta-analysis of cortical structure (33,992 participants of European ancestry) and AD (AD and AD-by-proxy, 71,880 cases, 383,378 controls). We found suggestive associations of the decreased surface area of the temporal pole (OR (95% CI): 0.95 (0.9, 0.997), p = 0.04), and decreased thickness of cuneus (OR (95% CI): 0.93 (0.89, 0.98), p = 0.006) with higher AD risk. We also found a suggestive association of vulnerability to AD with the decreased surface area of precentral (β (SE): -43.4 (21.3), p = 0.042) and isthmus cingulate (β (SE): -18.5 (7.3), p = 0.011). However, none of the Bonferroni-corrected p values of the causal relationship between cortical structure and AD met the threshold. We show suggestive evidence of an association of the atrophy of the temporal pole and cuneus with higher AD risk. In the other direction, there was a suggestive causal relationship between vulnerability to AD and the decreased surface area of the precentral and isthmus cingulate. Our findings shed light on the associations of cortical structure with the occurrence of AD.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Abnormal cortical regions and subsystems in whole brain functional connectivity of mild cognitive impairment and Alzheimer's disease: a preliminary study

The disease roots of Alzheimer's disease (AD) are unknown. Functional connection (FC) methodology based on functional MRI data is an effective lever to investigate macroscopic neural activity patterns. However, regional properties of brain architecture have been less investigated by special markers of graph indexes in general mental disorders. In terms of the set of the abnormal edges in the FCs matrix, this paper introduces the strength index (S-scores) of region centrality on the principle of holism. Then, the important process is to investigate the S-scores of regions and subsystems in 36 healthy controls, 38 mild cognitive impairment (MCI) patients and 34 AD patients. At the edge level, abnormal FCs is numerically increasing progressively from MCI to AD brains. At the region level, the CUN.L, PAL.R, THA.L, and TPOsup.R regions are highlighted with abnormal S-scores in MCI patients. By comparison, more regions are abnormal in AD patients, which are PreCG.L, INS.R, DCG.L, AMYG.R, IOG.R, FFG.L, PoCG.L, PCUN.R, TPOsup.L, MTG.L, and TPOmid.L. Importantly, the regions in DMN have abnormal S-scores in AD groups. At the module level, the S-scores of frontal, parietal, occipital lobe, and cerebellum are found in MCI and AD patients. Meanwhile, the abnormal lateralization is inferred because of the S-scores of left and top hemisphere in the AD group. Though this is strictly a contrastive study, the S-score may be a meaningful imaging marker for excavating AD psychopathology.

Medial temporal lobe volumes in late-life depression: effects of age and vascular risk factors

Substantial work associates late-life depression with hippocampal pathology. However, there is less information about differences in hippocampal subfields and other connected temporal lobe regions and how these regions may be influenced by vascular factors. Individuals aged 60 years or older with and without a DSM-IV diagnosis of Major Depressive Disorder completed clinical assessments and 3 T cranial MRI using a protocol allowing for automated measurement of medial temporal lobe subfield volumes. A subset also completed pseudo-continuous arterial spin labeling, allowing for the measurement of hippocampal cerebral blood flow. In 59 depressed and 21 never-depressed elders (mean age = 66.4 years, SD = 5.8y, range 60-86y), the depressed group did not exhibit statistically significant volumetric differences for the total hippocampus or hippocampal subfields but did exhibit significantly smaller volumes of the perirhinal cortex, specifically in the BA36 region. Additionally, age had a greater effect in the depressed group on volumes of the cornu ammonis, entorhinal cortex, and BA36 region. Finally, both clinical and radiological markers of vascular risk were associated with smaller BA36 volumes, while reduced hippocampal blood flow was associated with smaller hippocampal and cornu ammonis volumes. In conclusion, while we did not observe group differences in hippocampal regions, we observed group differences and an effect of vascular pathology on the BA36 region, part of the perirhinal cortex. This is a critical region exhibiting atrophy in prodromal Alzheimer's disease. Moreover, the observed greater effect of age in the depressed groups is concordant with past longitudinal studies reporting greater hippocampal atrophy in late-life depression.

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

Association of a CAMK2A genetic variant with logical memory performance and hippocampal volume in the elderly

Calcium/Calmodulin-dependent kinase alpha (αCaMKII) has been shown to play an essential role in synaptic plasticity and in learning and memory in animal models. However, there is little evidence for an involvement in specific memories in humans. Here we tested the potential involvement of the αCaMKII coding gene CAMK2A in verbal logical memory in two Caucasian populations from Germany, in a sample of 209 elderly people with cognitive impairments and a sample of 142 healthy adults. The association of single nucleotide polymorphisms (SNPs) located within the genomic region of CAMK2A with verbal logical memory learning and retrieval from the Wechsler Memory Scale was measured and hippocampal volume was assessed by structural MRI. In the elderly people, we found the minor allele of CAMK2A intronic SNP rs919741 to predict a higher hippocampal volume and better logical memory retrieval. This association was not found in healthy adults. The present study may provide evidence for an association of a genetic variant of the CAMK2A gene specifically with retrieval of logical memory in elderly humans. This effect is possibly mediated by a higher hippocampal volume.

Neuropathology of a remarkable case of memory impairment informs human memory

Kent Cochrane (K.C.) has been investigated by researchers for nearly three decades after intracranial trauma from a motorcycle accident at age 30 resulted in a striking profile of amnesia. K.C. suffered severe anterograde amnesia in both verbal and non-verbal domains which was accompanied by selective retrograde amnesia for personal events experienced prior to the time of his injury (episodic memory), with relative preservation of memory for personal and world facts (semantic memory), and of implicit memory. This pattern of spared and impaired memory extended to spatial memory for large-scale environments and beyond memory to future imagining and decision-making. Post-mortem brain findings at age 62 included moderate diffuse atrophy, left orbitofrontal contusion, left posterior cerebral artery infarct, and left anterior frontal watershed infarct. Notably, there was severe neuronal loss and gliosis of the hippocampi bilaterally. The left hippocampus was severely affected anteriorly and posteriorly, but CA2, CA4, and the dentate gyrus (DG) were focally spared. There was associated degeneration of the left fornix. The right hippocampus showed near complete destruction anteriorly, with relative preservation posteriorly, mainly of CA4 and DG. Bilateral parahippocampal gyri and left anterior thalamus also showed neuron loss and gliosis. There was no evidence of co-existing neurodegenerative phenomena on beta-amyloid, phosphorylated tau, or TDP-43 immunostaining. The extent of damage to medial temporal lobe structures is in keeping with K.C.'s profound anterograde and retrograde amnesia, with the exception of the unexpected finding of preserved CA2/CA4 and DG. K.C.'s case demonstrates that relatively clean functional dissociations are still possible following widespread brain damage, with structurally compromised brain regions unlikely to be critical to cognitive functions found to be intact. In this way, the findings presented here add to K.C.'s significant contributions to our understanding of clinical-anatomical relationships in memory.

Brain volumes and their ratios in Alzheimer´s disease on magnetic resonance imaging segmented using Freesurfer 6.0

Ratios between opposing volumes from brain magnetic resonance imaging (MRI) can provide additional information to volumes in Alzheimer's disease (AD). Brain three-dimensional MPRAGE MRI at 3T were segmented into 44 regions using FreeSurfer v6 in 75 participants. The region's size in absolute volumes and relative proportions to the whole brain volume were compared between 39 AD patients and 36 age-, education- and sex-matched normal controls (NC). Volumes of the most atrophied parts were related to the opposing volumes of the most enlarged parts as ratios. The most atrophic structures in AD were both hippocampi. By contrast, the greatest enlargements in AD were inferior parts of both lateral ventricles. The best ratio for each side was the hippocampo-horn proportion calculated as ratio: the hippocampus / (the hippocampus + inferior lateral ventricle). Its optimal cut-off of 74% yielded sensitivity of 74% and specificity of 78% on the left and sensitivity of 74% and specificity of 78% on the right. The hippocampo-horn proportion is another measure to evaluate the degree of hippocampal atrophy on brain MRI in percentages. It has a potential to be simplified into a comparison of two-dimensional corresponding areas or a visual assessment.

Microsurgical Anatomy of Medial Temporal Lobe in North-West Indian Population: Cadaveric Brain Dissection

Aim

The medial temporal lobe (MTL) is a highly complex neuroanatomical structure of tremendous neurosurgical importance. It is a common site for epilepsy, vascular lesions, and tumors. Owing to the critical location behind the sphenoid wing, it is more prone for traumatic contusion often with surgical implications. Hence, its microneurosurgical anatomy needs to be evaluated in detail.

Materials and Methods

Twelve formalin-fixed human cadaveric brains from North-west Indian population were dissected under neurosurgical microscope and various dimensions of the MTL and their distance from important neurovascular structures were measured.

Results

The MTL consists of important neural structures such as parahippocampal gyrus, uncus, hippocampus, temporal horn, and choroidal fissure. The average distance of tentorium from the uncus was 1.96 mm. The temporal horn and the inferior choroidal point were located from the anterior temporal pole at 22.9 mm and 30.9 mm, respectively. Important vessels that are intimately related to the MTL were anterior choroidal artery (AchA), posterior communicating artery, the P1 segment of posterior cerebral artery, and the M1 segment of middle cerebral artery.

Conclusion

Complex anatomic and cytostructural organization makes the MTL unique. In this study, along with the descriptive anatomy, morphometric measurements of various structures were performed. The uncus and its relation to other neurovascular structures is well described in literature, but its exact distance from them as determined in this study is particularly helpful in guiding the surgeons while approaching in this area. Knowledge of the distance of the temporal horn from various surfaces is important while opening the temporal horn to avoid unnecessary damage to nearby structures.

Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis

The mainstream of research in genetics, epigenetics, and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex phenotypes remains elusive. Using association analysis as a major analytical platform for the complex data analysis is a key issue that hampers the theoretic development of genomic science and its application in practice. Causal inference is an essential component for the discovery of mechanical relationships among complex phenotypes. Many researchers suggest making the transition from association to causation. Despite its fundamental role in science, engineering, and biomedicine, the traditional methods for causal inference require at least three variables. However, quantitative genetic analysis such as QTL, eQTL, mQTL, and genomic-imaging data analysis requires exploring the causal relationships between two variables. This paper will focus on bivariate causal discovery with continuous variables. We will introduce independence of cause and mechanism (ICM) as a basic principle for causal inference, algorithmic information theory and additive noise model (ANM) as major tools for bivariate causal discovery. Large-scale simulations will be performed to evaluate the feasibility of the ANM for bivariate causal discovery. To further evaluate their performance for causal inference, the ANM will be applied to the construction of gene regulatory networks. Also, the ANM will be applied to trait-imaging data analysis to illustrate three scenarios: presence of both causation and association, presence of association while absence of causation, and presence of causation, while lack of association between two variables. Telling cause from effect between two continuous variables from observational data is one of the fundamental and challenging problems in omics and imaging data analysis. Our preliminary simulations and real data analysis will show that the ANMs will be one of choice for bivariate causal discovery in genomic and imaging data analysis.

Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose.

OBJECTIVE

To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm.

METHODS

We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD.

RESULTS

The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI.

CONCLUSION

The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.

Effect of HMGCR genetic variation on neuroimaging biomarkers in healthy, mild cognitive impairment and Alzheimer's disease cohorts

Alzheimer's disease (AD) has become a considerable public health issue. The mechanisms underlying AD onset and progression remain largely unclear. 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) is a strong functional AD candidate gene because it encodes part of the statin-binding domain of the enzyme, which serves as the rate-limiting step in cholesterol synthesis in all mammalian cells. Here, we evaluated the potential role of HMGCR (rs3846662) in AD-related pathology by assessing neuroimaging biomarkers. We enrolled in 812 subjects from the Alzheimer's disease Neuroimaging Initiative dataset. In general, it is possible that HMGCR (rs3846662) could be involved in preventing the atrophy of right entorhinal (P=0.03385) and left hippocampus (P=0.01839) in the follow-up research of two years. What's more, it lowered the drop rate of glucose metabolism in right temporal. We then further validated them in the AD, mild cognitive impairment (MCI), normal control (NC) sub-groups. All the results in the MCI groups confirmed the association. The results of our study indicated that HMGCR (rs3846662) plays a vital role in AD pathology mainly by influencing brain structure and glucose metabolism during AD progression.

Magnetic resonance imaging and positron emission tomography in the diagnosis of neurodegenerative dementias

Neuroimaging, both with magnetic resonance imaging (MRI) and positron emission tomography (PET), has gained a pivotal role in the diagnosis of primary neurodegenerative diseases. These two techniques are used as biomarkers of both pathology and progression of Alzheimer's disease (AD) and to differentiate AD from other neurodegenerative diseases. MRI is able to identify structural changes including patterns of atrophy characterizing neurodegenerative diseases, and to distinguish these from other causes of cognitive impairment, e.g. infarcts, space-occupying lesions and hydrocephalus. PET is widely used to identify regional patterns of glucose utilization, since distinct patterns of distribution of cerebral glucose metabolism are related to different subtypes of neurodegenerative dementia. The use of PET in mild cognitive impairment, though controversial, is deemed helpful for predicting conversion to dementia and the dementia clinical subtype. Recently, new radiopharmaceuticals for the in vivo imaging of amyloid burden have been licensed and more tracers are being developed for the assessment of tauopathies and inflammatory processes, which may underlie the onset of the amyloid cascade. At present, the cerebral amyloid burden, imaged with PET, may help to exclude the presence of AD as well as forecast its possible onset. Finally PET imaging may be particularly useful in ongoing clinical trials for the development of dementia treatments. In the near future, the use of the above methods, in accordance with specific guidelines, along with the use of effective treatments will likely lead to more timely and successful treatment of neurodegenerative dementias.

Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices

Identifying Alzheimer’s disease (AD) at its early stage is of major interest in AD research. Previous studies have suggested that abnormalities in regional sulcal width and global sulcal index (g-SI) are characteristics of patients with early-stage AD. In this study, we investigated sulcal width and three other common neuroimaging morphological measures (cortical thickness, cortical volume, and subcortical volume) to identify early-stage AD. These measures were evaluated in 150 participants, including 75 normal controls (NC) and 75 patients with early-stage AD. The global sulcal index (g-SI) and the width of five individual sulci (the superior frontal, intra-parietal, superior temporal, central, and Sylvian fissure) were extracted from 3D T1-weighted images. The discriminative performances of the other three traditional neuroimaging morphological measures were also examined. Information Gain (IG) was used to select a subset of features to provide significant information for separating NC and early-stage AD subjects. Based on the four modalities of the individual measures, i.e., sulcal measures, cortical thickness, cortical volume, subcortical volume, and combinations of these individual measures, three types of classifiers (Naïve Bayes, Logistic Regression and Support Vector Machine) were applied to compare the classification performances. We observed that sulcal measures were either superior than or equal to the other measures used for classification. Specifically, the g-SI and the width of the Sylvian fissure were two of the most sensitive sulcal measures and could be useful neuroanatomical markers for detecting early-stage AD. There were no significant differences between the three classifiers that we tested when using the same neuroanatomical features.

Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis

Quantitatively assessing the medial temporal lobe (MTL) structures atrophy is vital for early diagnosis of Alzheimer's disease (AD) and accurately tracking of the disease progression. Morphometry characteristics such as gray matter volume (GMV) and cortical thickness have been proved to be valuable measurements of brain atrophy. In this study, we proposed a morphometric MRI analysis based method to explore the cross-sectional differences and longitudinal changes of GMV and cortical thickness in patients with AD, MCI (mild cognitive impairment) and the normal elderly. High resolution 3D MRI data was obtained from ADNI database. SPM8 plus DARTEL was carried out for data preprocessing. Two kinds of z-score map were calculated to, respectively, reflect the GMV and cortical thickness decline compared with age-matched normal control database. A volume of interest (VOI) covering MTL structures was defined by group comparison. Within this VOI, GMV, and cortical thickness decline indicators were, respectively, defined as the mean of the negative z-scores and the sum of the normalized negative z-scores of the corresponding z-score map. Kruskal-Wallis test was applied to statistically identify group wise differences of the indicators. Support vector machines (SVM) based prediction was performed with a leave-one-out cross-validation design to evaluate the predictive accuracies of the indicators. Linear least squares estimation was utilized to assess the changing rate of the indicators for the three groups. Cross-sectional comparison of the baseline decline indicators revealed that the GMV and cortical thickness decline were more serious from NC, MCI to AD, with statistic significance. Using a multi-region based SVM model with the two indicators, the discrimination accuracy between AD and NC, MCI and NC, AD and MCI was 92.7, 91.7, and 78.4%, respectively. For three-way prediction, the accuracy was 74.6%. Furthermore, the proposed two indicators could also identify the atrophy rate differences among the three groups in longitudinal analysis. The proposed method could serve as an automatic and time-sparing approach for early diagnosis and tracking the progression of AD.

Brain atrophy in Alzheimer's Disease and aging

Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.

Atrophy and structural covariance of the cholinergic basal forebrain in primary progressive aphasia

Primary progressive aphasia (PPA) is characterized by profound destruction of cortical language areas. Anatomical studies suggest an involvement of cholinergic basal forebrain (BF) in PPA syndromes, particularly in the area of the nucleus subputaminalis (NSP). Here we aimed to determine the pattern of atrophy and structural covariance as a proxy of structural connectivity of BF nuclei in PPA variants. We studied 62 prospectively recruited cases with the clinical diagnosis of PPA and 31 healthy older control participants from the cohort study of the German consortium for frontotemporal lobar degeneration (FTLD). We determined cortical and BF atrophy based on high-resolution magnetic resonance imaging (MRI) scans. Patterns of structural covariance of BF with cortical regions were determined using voxel-based partial least square analysis. We found significant atrophy of total BF and BF subregions in PPA patients compared with controls [F(1, 82) = 20.2, p < .001]. Atrophy was most pronounced in the NSP and the posterior BF, and most severe in the semantic variant and the nonfluent variant of PPA. Structural covariance analysis in healthy controls revealed associations of the BF nuclei, particularly the NSP, with left hemispheric predominant prefrontal, lateral temporal, and parietal cortical areas, including Broca's speech area (p < .001, permutation test). In contrast, the PPA patients showed preserved structural covariance of the BF nuclei mostly with right but not with left hemispheric cortical areas (p < .001, permutation test). Our findings agree with the neuroanatomically proposed involvement of the cholinergic BF, particularly the NSP, in PPA syndromes. We found a shift from a structural covariance of the BF with left hemispheric cortical areas in healthy aging towards right hemispheric cortical areas in PPA, possibly reflecting a consequence of the profound and early destruction of cortical language areas in PPA.

Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans

Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is critical for identification of patients who are at risk for Alzheimer's disease (AD), so that early treatment can be administered. In this paper, we propose a pMCI/sMCI classification framework that harnesses information available in longitudinal magnetic resonance imaging (MRI) data, which could be incomplete, to improve diagnostic accuracy. Volumetric features were first extracted from the baseline MRI scan and subsequent scans acquired after 6, 12, and 18 months. Dynamic features were then obtained using the 18th month scan as the reference and computing the ratios of feature differences for the earlier scans. Features that are linearly or non-linearly correlated with diagnostic labels are then selected using two elastic net sparse learning algorithms. Missing feature values due to the incomplete longitudinal data are imputed using a low-rank matrix completion method. Finally, based on the completed feature matrix, we build a multi-kernel support vector machine (mkSVM) to predict the diagnostic label of samples with unknown diagnostic statuses. Our evaluation indicates that a diagnosis accuracy as high as 78.2 % can be achieved when information from the longitudinal scans is used-6.6 % higher than the case using only the reference time point image. In other words, information provided by the longitudinal history of the disease improves diagnosis accuracy.

Quantitative Measurements in the Human Hippocampus and Related Areas: Correspondence between Ex-Vivo MRI and Histological Preparations

The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlate with the decline of cognitive functions in neurodegenerative diseases. This study presents data on the association between MRI quantitative parameters of medial temporal lobe structures and their quantitative estimate in microscopic examination. Twelve control cases had ex-vivo MRI, and thereafter, the temporal lobe of both hemispheres was sectioned from the pole as far as the level of the splenium of the corpus callosum. Nissl stain was used to establish anatomical boundaries between structures in the medial temporal lobe. The study included morphometrical and stereological estimates of the amygdaloid complex, hippocampus, and temporal horn of the lateral ventricle, as well as different regions of grey and white matter in the temporal lobe. Data showed a close association between morphometric MRI images values and those based on the histological determination of boundaries. Only values in perimeter and circularity of the piamater were different. This correspondence is also revealed by the stereological study, although irregular compartments resulted in a lesser agreement. Neither age (< 65 yr and > 65 yr) nor hemisphere had any effect. Our results indicate that ex-vivo MRI is highly associated with quantitative information gathered by histological examination, and these data could be used as structural MRI biomarker in neurodegenerative diseases.

Influence of genetic variants in SORL1 gene on the manifestation of Alzheimer's disease

We studied the association of SORL1 single-nucleotide polymorphisms genotypes with measures of pathology in patients with probable Alzheimer's disease (AD) using an endophenotype approach. We included (1) 133 patients from the German Dementia Competence Network (71 ± 8 years; 50% females; Mini Mental State Examination [MMSE], 24 ± 3); (2) 83 patients from the Alzheimer's Disease Neuroimaging Initiative (75 ± 8 years; 45% females; MMSE, 24 ± 2); and (3) 452 patients from the Amsterdam Dementia Cohort 66 ± 8 years; 47% females; MMSE, 20 ± 5). As endophenotype markers we used cognitive tests, cerebrospinal fluid (CSF) biomarkers amyloid-beta, total tau (tau), tau phosphorylated at threonine 181, and hippocampal atrophy. We measured 19 SORL1 SNP alleles. Genotype-endophenotype associations were determined by linear regression analyses. There was an association between rs2070045-G allele and increased CSF-tau and more hippocampal atrophy. Additionally, haplotype-based analyses revealed an association between haplotype rs11218340-A/rs3824966-G/rs3824968-A and higher CSF-tau and CSF-tau phosphorylated at threonine 181. In conclusion, we found that SORL1 SNP rs2070045-G allele was related to CSF-tau and hippocampal atrophy, 2 endophenotype markers of AD, suggesting that SORL1 may be implicated in the downstream pathology in AD.

Association of age at depression onset with cognitive functioning in individuals with late-life depression and executive dysfunction

OBJECTIVE

To compare patterns of cognitive performance in older adults with late-onset depression (LOD; ≥65 years of age) with that of older adults with early-onset depression (EOD; <65 years).

METHODS

Participants were 171 adults aged 60 years or older with major depression and executive dysfunction who were participating in a randomized psychotherapy trial. Participants included 72 LOD and 99 EOD individuals. Cognitive performance on measures of verbal learning, memory, and executive functioning were evaluated. Demographic and clinical characteristics, severity of cerebrovascular risk factors, and disability ratings were also compared between groups.

RESULTS

The LOD group was older and had fewer previous episodes of depression and lower severity of depression compared with EOD participants. The LOD group demonstrated poorer performance on measures of verbal learning (F(1,161) = 4.28, p = 0.04) and memory (F(1,160) = 4.65, p = 0.03) than the EOD group. Linear regression analysis demonstrated that LOD and fewer years of education were significant predictors of poorer verbal learning (F(7,114) = 6.25, p <0.001) and memory (F(7,113)=7.24, p <0.001). Performance on measures of executive functioning, severity of vascular risk factors, and disability ratings did not differ between the two groups.

CONCLUSION

In older adults with depression and executive dysfunction, LOD was associated with poorer performance on measures of verbal learning and memory. Aging-related brain changes associated with LOD may play a more important role, leading to dysfunction in these cognitive domains than a history of recurrent depressive episodes in older adults with a dysexecutive syndrome.

Discriminative analysis of multivariate features from structural MRI and diffusion tensor images

Imaging markers derived from magnetic resonance images, together with machine learning techniques allow for the recognition of unique anatomical patterns and further differentiating Alzheimer's disease (AD) from normal states. T1-based imaging markers, especially volumetric patterns have demonstrated their discriminative potential, however, rely on the tissue abnormalities of gray matter alone. White matter abnormalities and their contribution to AD discrimination have been studied by measuring voxel-based intensities in diffusion tensor images (DTI); however, no systematic study has been done on the discriminative power of either region-of-interest (ROI)-based features from DTI or the combined features extracted from both T1 images and DTI. ROI-based analysis could potentially reduce the feature dimensionality of DTI indices, usually from more than 10e+5, to 10-150 which is almost equal to the order of magnitude with respect to volumetric features from T1. Therefore it allows for straight forward combination of intensity based landmarks of DTI indices and volumetric features of T1. In the present study, the feasibility of tract-based features related to Alzheimer's disease was first evaluated by measuring its discriminative capability using support vector machine on fractional anisotropy (FA) maps collected from 21 subjects with Alzheimer's disease and 15 normal controls. Then the performance of the tract-based FA+gray matter volumes-combined feature was evaluated by cross-validation. The combined feature yielded good classification result with 94.3% accuracy, 95.0% sensitivity, 93.3% specificity, and 0.96 area under the receiver operating characteristic curve. The tract-based FA and the tract-based FA+gray matter volumes-combined features are certified their feasibilities for the recognition of anatomical features and may serve to complement classification methods based on other imaging markers.

Comparison of medial temporal measures between Binswanger's disease and Alzheimer's disease

Binswanger's disease (BD) is a common cause of vascular dementia in elderly patients; however, few studies have investigated the medial temporal lobe (MTL) atrophy in BD, and the differences in the atrophic patterns between BD and Alzheimer's disease (AD) remain largely unknown. Such knowledge is essential for understanding the pathologic basis of dementia. In this study, we collected structural magnetic resonance imaging (MRI) data from 16 normal controls, 14 patients with AD and 14 patients with BD. The volumes of the hippocampus and amygdala, and morphologic parameters (volume, surface area, cortical thickness and mean curvature) of the entorhinal cortex (ERC) and perirhinal cortex (PRC) were calculated using an automated approach. Volume reduction of the hippocampus, amygdala and ERC, and disturbance of the PRC curvature was found in both AD and BD patients compared with the controls (p<0.05, uncorrected). There were no significant differences among all the structural measures between the AD and BD patients. Finally, partial correlation analyses revealed that cognitive decline could be attributed to ERC thinning in AD and volume reduction of PRC in BD. We conclude that AD and BD exhibit similar atrophy patterns in the medial temporal cortices and deep gray matter but have distinct pathologic bases for cognitive impairments. Although atrophy of the MTL structures is a sensitive biomarker for AD, it is not superior for discrimination between AD and BD.

Comparing new templates and atlas-based segmentations in the volumetric analysis of brain magnetic resonance images for diagnosing Alzheimer's disease

BACKGROUND

The segmentation of brain structures on magnetic resonance imaging scans for calculating regional brain volumes, using automated anatomic labeling, requires the use of both brain atlases and templates (template sets). This study aims to improve the accuracy of volumetric analysis of hippocampus (HP) and amygdala (AMG) in the assessment of early Alzheimer's disease (AD) by developing template sets that correspond more closely to the brains of elderly individuals.

METHODS

Total intracranial volume and HP and AMG volumes were calculated for elderly subjects with no cognitive impairment (n = 103), with amnestic mild cognitive impairment (n = 68), or with probable AD (n = 46) using the following: (1) a template set consisting of a standard atlas (atlas S), drawn on a young adult male brain, and the widely used Montreal Neurological Institute template (MNI template set); (2) a template set (template S set) in which the template is based on smoothing the image from which atlas S is derived; and (3) a new template set (template E set) in which the template is based on an atlas (atlas E) created from the brain of an elderly individual.

RESULTS

Correspondence to HP and AMG volumes derived from manual segmentation was highest with automated segmentation by template E set, intermediate with template S set, and lowest with the MNI template set. The areas under the receiver operating curve for distinguishing elderly subjects with no cognitive impairment from elderly subjects with amnestic mild cognitive impairment or probable AD and the correlations between HP and AMG volumes and cognitive and functional scores were highest for template E set, intermediate for template S set, and lowest for the MNI template set.

CONCLUSIONS

The accuracy of automated anatomic labeling and the diagnostic value of the derived volumes are improved with template sets based on brain atlases closely resembling the anatomy of the to-be-segmented brain magnetic resonance imaging scans.

Do Alzheimer-specific microstructural changes in mild cognitive impairment predict conversion?

Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that provides information on the fiber architecture of the brain by measuring water diffusion. Prior work has shown that neuronal degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) alters this architecture. Since the conversion rate to AD is much higher for MCI patients than for normal healthy people, it is important to identify biomarkers with a predictive value on this conversion. In this study, we applied tract-based spatial statistics (TBSS) on datasets of 15 healthy controls, 15 AD patients, and 17 MCI patients. Of these MCI patients eight remained stable, whereas nine developed AD within the first 12-18 months of follow-up investigations. Analysis using TBSS combined with a maximum likelihood regression with random effects of the fornix, the corpus callosum, and the cingulum identified significant differences between these two types of MCI patients in fractional anisotropy (FA) and radial diffusivity (DR). Thus, DTI reveals Alzheimer-specific changes in those MCI subjects that later convert, although they were clinically identical to the other MCI-patients at the time the data were acquired. This finding could lead to early identification of AD and thereby aid early clinical intervention.

Posterior cerebral atrophy in the absence of medial temporal lobe atrophy in pathologically-confirmed Alzheimer's disease

Medial temporal lobe atrophy (MTA) is a recognized marker of Alzheimer's disease (AD), however, it can be prominent in frontotemporal lobar degeneration (FTLD). There is an increasing awareness that posterior atrophy (PA) is important in AD and may aid the differentiation of AD from FTLD. Visual rating scales are a convenient way of assessing atrophy in a clinical setting. In this study, 2 visual rating scales measuring MTA and PA were used to compare atrophy patterns in 62 pathologically-confirmed AD and 40 FTLD patients. Anatomical correspondence of MTA and PA was assessed using manually-delineated regions of the hippocampus and posterior cingulate gyrus, respectively. Both MTA and PA scales showed good inter- and intrarater reliabilities (kappa > 0.8). MTA scores showed a good correspondence with manual hippocampal volumes. Thirty percent of the AD patients showed PA in the absence of MTA. Adding the PA to the MTA scale improved discrimination of AD from FTLD, and early-onset AD from normal aging. These results underline the importance of considering PA in AD diagnosis, particularly in younger patients where medial temporal atrophy may be less conspicuous.

Frontotemporal lobar degeneration-related proteins induce only subtle memory-related deficits when bilaterally overexpressed in the dorsal hippocampus

Frontotemporal lobar degeneration (FTLD) is a neurodegenerative disease that involves cognitive decline and dementia. To model the hippocampal neurodegeneration and memory-related behavioral impairment that occurs in FTLD and other tau and TDP-43 proteinopathy diseases, we used an adeno-associated virus serotype 9 (AAV9) vector to induce bilateral expression of either microtubule-associated protein tau or transactive response DNA binding protein 43 kDa (TDP-43) in adult rat dorsal hippocampus. Human wild-type forms of tau or TDP-43 were expressed. The vectors/doses were designed for moderate expression levels within neurons. Rats were evaluated for acquisition and retention in the Morris water task over 12 weeks after gene transfer. Neither vector altered acquisition performance compared to controls. In measurements of retention, there was impairment in the TDP-43 group. Histological examination revealed specific loss of dentate gyrus granule cells and concomitant gliosis proximal to the injection site in the TDP-43 group, with shrinkage of the dorsal hippocampus. Despite specific tau pathology, the tau gene transfer surprisingly did not cause obvious neuronal loss or behavioral impairment. The data demonstrate that TDP-43 produced mild behavioral impairment and hippocampal neurodegeneration in rats, whereas tau did not. The models could be of value for studying mechanisms of FTLD and other diseases with tau and TDP-43 pathology in the hippocampus including Alzheimer's disease, with relevance to early stage mild impairment.

A robust volumetric feature extraction approach for 3D neuroimaging retrieval

The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric neurological images. Our preliminary results indicate that the proposed volumetric feature extraction approach could support reliable 3D neuroimaging data retrieval and management.

Sub-millimeter isotropic MRI for segmentation of subcortical brain regions and brain visualization

PURPOSE

To evaluate a rapid sub-millimeter isotropic spoiled gradient-echo (nonselective SPGR) to facilitate the brain subcortical segmentation and the visualization of brain volume compared with the commonly accepted inversion recovery-prepared SPGR (SPGR-IR) technique.

MATERIALS AND METHODS

The feasibility of the nonselective SPGR was evaluated for two segmentation algorithms. FAST was used to segment the brain into constituent tissue classes (white matter, gray matter, cerebrospinal fluid) and FreeSurfer was used to segment specific subcortical structures (hippocampus, caudate, putamen, and thalamus). Localized apparent signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values for nonselective SPGR and the SPGR-IR were compared for the studied subcortical regions. The three-dimensional volume rendering was generated to evaluate the nonselective SPGR and the SPGR-IR for brain visualization.

RESULTS

In basal ganglia regions, nonselective SPGR allows for consistent segmentation results for both FAST and FreeSurfer. This sequence also better differentiated gray/white matter compared with SPGR-IR. An approximate two-fold improvement of image quality in apparent SNR and CNR was indicated for subcortical brain anatomical structures with nonselective SPGR versus SPGR-IR. The nonselective SPGR improved clarity and yielded a more realistic depiction of the brain surface for visualization compared with SPGR-IR.

CONCLUSION

Compared with SPGR-IR, nonselective SPGR allows for consistent segmentation results for basal ganglia regions and improved clarity for visualization of the brain.

Regionally selective atrophy after traumatic axonal injury

OBJECTIVES

To determine the spatial distribution of cortical and subcortical volume loss in patients with diffuse traumatic axonal injury and to assess the relationship between regional atrophy and functional outcome.

DESIGN

Prospective imaging study. Longitudinal changes in global and regional brain volumes were assessed using high-resolution magnetic resonance imaging-based morphometric analysis.

SETTING

Inpatient traumatic brain injury unit.

PATIENTS OR OTHER PARTICIPANTS

Twenty-five patients with diffuse traumatic axonal injury and 22 age- and sex-matched controls.

MAIN OUTCOME MEASURE

Changes in global and regional brain volumes between initial and follow-up magnetic resonance imaging were used to assess the spatial distribution of posttraumatic volume loss. The Glasgow Outcome Scale-Extended score was the primary measure of functional outcome.

RESULTS

Patients underwent substantial global atrophy with mean whole-brain parenchymal volume loss of 4.5% (95% confidence interval, 2.7%-6.3%). Decreases in volume (at a false discovery rate of 0.05) were seen in several brain regions including the amygdala, hippocampus, thalamus, corpus callosum, putamen, precuneus, postcentral gyrus, paracentral lobule, and parietal and frontal cortices, while other regions such as the caudate and inferior temporal cortex were relatively resistant to atrophy. Loss of whole-brain parenchymal volume was predictive of long-term disability, as was atrophy of particular brain regions including the inferior parietal cortex, pars orbitalis, pericalcarine cortex, and supramarginal gyrus.

CONCLUSION

Traumatic axonal injury leads to substantial posttraumatic atrophy that is regionally selective rather than diffuse, and volume loss in certain regions may have prognostic value for functional recovery.

Multicentre variability of MRI-based medial temporal lobe volumetry in Alzheimer's disease

Magnetic resonance imaging (MRI)-based volumetry of medial temporal lobe regions is among the best established biomarker candidates of Alzheimer's disease (AD) to date. This study assessed the effect of multicentre variability of MRI-based hippocampus and amygdala volumetry on the discrimination between patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and on the association of morphological changes with ApoE4 genotype and cognition. We studied 113 patients with clinically probable AD and 150 patients with amnestic MCI using high-resolution MRI scans obtained at 12 clinical sites. We determined effect sizes of group discrimination and random effects linear models, considering multicentre variability. Hippocampus and amygdala volumes were significantly reduced in AD compared with MCI patients using data pooled across centres. Multicentre variability did not significantly affect the power to detect a volume difference between AD and MCI patients. Among cognitive measures, delayed recall of verbal and non-verbal material was significantly correlated with hippocampus and amygdala volumes. Amygdala and hippocampus volumes were not associated with ApoE4 genotype in AD or MCI. Our data indicate that multicentre acquisition of MRI data using manual volumetry is reliable and feasible for cross-sectional diagnostic studies, and they replicate essential findings from smaller scale monocentre studies.

White matter integrity in mild cognitive impairment: a tract-based spatial statistics study

Mild cognitive impairment (MCI) as a clinical diagnosis has limited specificity, and identifying imaging biomarkers may improve its predictive validity as a pre-dementia syndrome. This study used diffusion tensor imaging (DTI) to detect white matter (WM) structural alterations in MCI and its subtypes, and aimed to examine if DTI can serve as a potential imaging marker of MCI. We studied 96 amnestic MCI (aMCI), 69 non-amnestic MCI (naMCI), and 252 cognitively normal (CN) controls. DTI was performed to measure fractional anisotropy (FA), and tract-based spatial statistics (TBSS) were applied to investigate the characteristics of WM changes in aMCI and naMCI. The diagnostic utility of DTI in distinguishing MCI from CN was further evaluated by using a binary logistic regression model. We found that FA was significantly reduced in aMCI and naMCI when compared with CN. For aMCI subjects, decreased FA was seen in the frontal, temporal, parietal, and occipital WM, together with several commissural, association, and projection fibres. The best discrimination between aMCI and controls was achieved by combining FA measures of the splenium of corpus callosum and crus of fornix, with accuracy of 74.8% (sensitivity 71.0%, specificity 76.2%). For naMCI subjects, WM abnormality was more anatomically widespread, but the temporal lobe WM was relatively spared. These results suggest that aMCI is best characterized by pathology consistent with early Alzheimer's disease, whereas underlying pathology in naMCI is more heterogeneous, and DTI analysis of white matter structural integrity can serve as a potential biomarker of MCI and its subtypes.