Hippocampus-precuneus functional connectivity as an early sign of Alzheimer's disease: a preliminary study using structural and functional magnetic resonance imaging data

Disruptive resting state networks characterizing depressive comorbidity in Alzheimer's disease and mild cognitive impairment

BackgroundDepressive comorbidity in neurodegeneration has been shown to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, its pathophysiology is not completely understood.ObjectiveHere, we characterize aberrant functional resting state networks (RSNs) characterizing depressive comorbidity in both AD and MCI.MethodsWe conducted a systematic literature review on Scopus, PubMed, and Web of Science to extract experiments that compared resting state scans of depressed and non-depressed MCI or AD patients. We employed Activation Likelihood Estimation (ALE) meta-analysis on eligible studies resulting from the search, to describe regions of significant co-activation across studies.ResultsThe systematic search resulted in 17 experiments, with 303 participants in total. The ALE yielded 10 clusters of significant co-activation distributed in the five major RSNs and across cortico-basal ganglia-thalamic circuits.ConclusionsDepressive comorbidity in neurodegeneration presents signature aberrant resting-state fluctuations. Understanding these within- and between-network alterations may be useful for future diagnostic and therapeutic applications.

Hippocampal functional imaging-derived radiomics features for diagnosing cognitively impaired patients with Parkinson's disease

PURPOSE

The aim of this retrospective study was to investigate whether radiomics features derived from hippocampal functional imaging can effectively differentiate cognitively impaired patients from cognitively preserved patients with Parkinson's disease (PD).

METHODS

The study included a total of 89 clinically definite PD patients, comprising 55 who werecognitively impaired and 34 who were cognitively preserved. All participants underwent functional magnetic resonance imaging and clinical assessments. Preprocessed functional data were utilized to derive the amplitude of the low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). A standardized set of radiomics features was subsequently extracted from the bilateral hippocampi, resulting in a total of 819 features. Following feature selection, the radiomics score (rad-score) and logistic regression (LR) models were trained. Additionally, the Shapley additive explanations (SHAP) algorithm was employed to elucidate and interpret the predictions made by the LR models. Finally, the relationships between the radiomics features derived from hippocampal functional imaging and the scores of the clinical measures were explored to assess their clinical significance.

RESULTS

The rad-score and LR algorithm models constructed using a combination of wavelet features extracted from ReHo and VMHC data exhibited superior classification efficiency. These models demonstrated exceptional accuracy, sensitivity, and specificity in distinguishing cognitively impaired PD patients (CI-PD) from cognitively preserved PD (CP-PD) patients, with values of 0.889, 0.900, and 0.882, respectively. Furthermore, SHAP values indicated that wavelet features derived from ReHo and VMHC were critical for classifying CI-PD patients. Importantly, our findings revealed significant associations between radiomics wavelet features and scores on the Hamilton Anxiety Scale, Non-Motor Symptom Scale, and Montreal Cognitive Assessment in CI-PD patients (P < 0.05, with Bonferroni correction).

CONCLUSIONS

Our novel rad-score model and LR model, which utilize radiomics features derived from hippocampal functional imaging, have demonstrated their value in diagnosing CI-PDpatients. These models can enhance the accuracy and efficiency of functional MRI diagnosis, suggesting potential clinical applications.

CLINICAL TRIAL NUMBER

Not applicable.

Impact of sleep disruptions on gray matter structural covariance networks across the Alzheimer's disease continuum

BACKGROUND

This study explores the impact of sleep disturbances on gray matter structural covariance networks (SCNs) across the Alzheimer's disease (AD) continuum.

METHODS

Amyloid-negative participants served as controls, whereas amyloid positive (A+) individuals were categorized into six groups based on cognitive status and sleep quality. SCNs for the default mode network (DMN), salience network (SN), and executive control network (ECN) were derived from T1-weighted magnetic resonance images.

RESULTS

In the DMN, increased structural associations were observed in cognitive unimpaired (CU) A+ and mild cognitive impairment (MCI) groups regardless of sleep quality, whereas AD with poor sleep (PS) showed a decrease and AD with normal sleep (NS) an increase. For the ECN, AD-NS showed increased and AD-PS showed reduced associations. In the SN, reduced associations were observed in CU A+ NS and MCI-NS, whereas AD-NS displayed increased associations; only AD-PS had decreased associations.

CONCLUSION

Distinct SCN damage patterns between normal and poor sleepers provide insights into sleep disturbances in AD.

Highlights

We delineated distinct patterns of structural covariance networks (SCN) impairment across the Alzheimer's disease (AD) continuum, uncovering significant disparities between individuals with normal sleep architecture and those afflicted by sleep disturbances.These observations underscore the pivotal importance of addressing sleep disruptions in AD therapeutics, providing a refined understanding of their detrimental impact on brain networks implicated in the disease.Our investigation epitomizes methodological precision by constructing an AD continuum using amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers to minimize diagnostic heterogeneity, further enhanced by a substantial cohort size that bolsters the robustness and generalizability of our findings.

Differences of regional homogeneity and cognitive function between psychotic depression and drug-naïve schizophrenia

BACKGROUND

Psychotic depression (PD) and schizophrenia (SCZ) share overlapping symptoms yet differ in etiology, progression, and treatment approaches. Differentiating these disorders through symptom-based diagnosis is challenging, emphasizing the need for a clearer understanding of their distinct cognitive and neural mechanisms.

AIM

This study aims to compare cognitive impairments and brain functional activities in PD and SCZ to pinpoint distinguishing characteristics of each disorder.

METHODS

We evaluated cognitive function in 42 PD and 30 SCZ patients using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) values were derived from rs-fMRI data, and group differences in RBANS scores were analyzed. Additionally, Pearson correlation analysis was performed to assess the relationship between cognitive domains and brain functional metrics.

RESULTS

(1) The SCZ group showed significantly lower RBANS scores than the PD group across all cognitive domains, particularly in visuospatial/constructional ability and delayed memory (p < 0.05); (2) The SCZ group exhibited a significantly higher ReHo value in the left precuneus compared to the PD group (p < 0.05); (3) A negative correlation was observed between visuospatial construction, delayed memory scores, and the ReHo value of the left precuneus.

CONCLUSION

Cognitive impairment is more pronounced in SCZ than in PD, with marked deficits in visuospatial and memory domains. Enhanced left precuneus activity further differentiates SCZ from PD and correlates with cognitive impairments in both disorders, providing neuroimaging-based evidence to aid differential diagnosis and insights into cognitive dysfunction mechanisms, while also paving a clearer path for psychiatric research.

Functional connectivity favors aberrant visual network c-Fos expression accompanied by cortical synapse loss in a mouse model of Alzheimer's disease

While Alzheimer's disease (AD) has been extensively studied with a focus on cognitive networks, sensory network dysfunction has received comparatively less attention despite compelling evidence of its significance in both Alzheimer's disease patients and mouse models. We recently found that neurons in the primary visual cortex of an AD mouse model expressing human amyloid protein precursor with the Swedish and Indiana mutations (hAPP mutations) exhibit aberrant c-Fos expression and altered synaptic structures at a pre-amyloid plaque stage. However, it is unclear whether aberrant c-Fos expression and synaptic pathology vary across the broader visual network and to what extent c-Fos abnormality in the cortex is inherited through functional connectivity. Using both sexes of 4-6-month AD model mice with hAPP mutations (J20[PDGF-APPSw, Ind]), we found that cortical regions of the visual network show aberrant c-Fos expression and impaired experience-dependent modulation while subcortical regions do not. Interestingly, the average network-wide functional connectivity strength of a brain region in wild type (WT) mice significantly predicts its aberrant c-Fos expression, which in turn correlates with impaired experience-dependent modulation in the AD model. Using in vivo two-photon and ex vivo imaging of presynaptic termini, we observed a subtle yet selective weakening of excitatory cortical synapses in the visual cortex. Intriguingly, the change in the size distribution of cortical boutons in the AD model is downscaled relative to those in WT mice, suggesting that synaptic weakening may reflect an adaptation to aberrant activity. Our observations suggest that cellular and synaptic abnormalities in the AD model represent a maladaptive transformation of the baseline physiological state seen in WT conditions rather than entirely novel and unrelated manifestations.

The effect of the apolipoprotein E ε4 allele and olfactory function on odor identification networks

INTRODUCTION

The combination of apolipoprotein E ε4 (ApoE ε4) status, odor identification, and odor familiarity predicts conversion to mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

To further understand olfactory disturbances and AD risk, ApoE ε4 carrier (mean age 76.38 ± 5.21) and ε4 non-carrier (mean age 76.8 ± 3.35) adults were given odor familiarity and identification tests and performed an odor identification task during fMRI scanning. Five task-related functional networks were detected using independent components analysis. Main and interaction effects of mean odor familiarity ratings, odor identification scores, and ε4 status on network activation and task-modulation of network functional connectivity (FC) during correct and incorrect odor identification (hits and misses), controlling for age and sex, were explored using multiple linear regression.

RESULTS

Findings suggested that sensory-olfactory network activation was positively associated with odor identification scores in ε4 carriers with intact odor familiarity. The FC of sensory-olfactory, multisensory-semantic integration, and occipitoparietal networks was altered in ε4 carriers with poorer odor familiarity and identification. In ε4 carriers with poorer familiarity, connectivity between superior frontal areas and the sensory-olfactory network was negatively associated with odor identification scores.

CONCLUSIONS

The results contribute to the clarification of the neurocognitive structure of odor identification processing and suggest that poorer odor familiarity and identification in ε4 carriers may signal multi-network dysfunction. Odor familiarity and identification assessment in ε4 carriers may contribute to the predictive value of risk for MCI and AD due to the breakdown of sensory-cognitive network integration. Additional research on olfactory processing in those at risk for AD is warranted.

A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease

There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer's Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Disconnectome associated with progressive white matter hyperintensities in aging: a virtual lesion study

Objective

White matter hyperintensities (WMH) are commonly seen on T2-weighted magnetic resonance imaging (MRI) in older adults and are associated with an increased risk of cognitive decline and dementia. This study aims to estimate changes in the structural connectome due to age-related WMH by using a virtual lesion approach.

Methods

High-quality diffusion-weighted imaging data of 30 healthy subjects were obtained from the Human Connectome Project (HCP) database. Diffusion tractography using q-space diffeomorphic reconstruction (QSDR) and whole brain fiber tracking with 107 seed points was conducted using diffusion spectrum imaging studio and the brainnetome atlas was used to parcellate a total of 246 cortical and subcortical nodes. Previously published WMH frequency maps across age ranges (50's, 60's, 70's, and 80's) were used to generate virtual lesion masks for each decade at three lesion frequency thresholds, and these virtual lesion masks were applied as regions of avoidance (ROA) in fiber tracking to estimate connectivity changes. Connections showing significant differences in fiber density with and without ROA were identified using paired tests with False Discovery Rate (FDR) correction.

Results

Disconnections appeared first from the striatum to middle frontal gyrus (MFG) in the 50's, then from the thalamus to MFG in the 60's and extending to the superior frontal gyrus in the 70's, and ultimately including much more widespread cortical and hippocampal nodes in the 80's.

Conclusion

Changes in the structural disconnectome due to age-related WMH can be estimated using the virtual lesion approach. The observed disconnections may contribute to the cognitive and sensorimotor deficits seen in aging.

Improved connectivity and cognition due to cognitive stimulation in Alzheimer's disease

Background

Due to the increasing prevalence of Alzheimer's disease (AD) and the limited efficacy of pharmacological treatment, the interest in non-pharmacological interventions, e.g., cognitive stimulation therapy (CST), to improve cognitive dysfunction and the quality of life of AD patients are on a steady rise.

Objectives

Here, we examined the efficacy of a CST program specifically conceptualized for AD dementia patients and the neural mechanisms underlying cognitive or behavioral benefits of CST.

Methods

Using neuropsychological tests and MRI-based measurements of functional connectivity, we examined the (neuro-) psychological status and network changes at two time points: pre vs. post-stimulation (8 to 12 weeks) in the intervention group (n = 15) who received the CST versus a no-intervention control group (n = 15).

Results

After CST, we observed significant improvement in the Mini-Mental State Examination (MMSE), the Alzheimer's Disease Assessment Scale, cognitive subsection (ADAS-cog), and the behavioral and psychological symptoms of dementia (BPSD) scores. These cognitive improvements were associated with an up-regulated functional connectivity between the left posterior hippocampus and the trunk of the left postcentral gyrus.

Conclusion

Our data indicate that CST seems to induce short-term global cognition and behavior improvements in mild to moderate AD dementia and enhances resting-state functional connectivity in learning- and memory-associated brain regions. These convergent results prove that even in mild to moderate dementia AD, neuroplasticity can be harnessed to alleviate cognitive impairment with CST.

Effects of the SNAP-25 Mnll variant on hippocampal functional connectivity in children with attention deficit/hyperactivity disorder

Objectives

Attention-deficit/hyperactivity disorder (ADHD) is one of the most widespread and highly heritable neurodevelopmental disorders affecting children worldwide. Although synaptosomal-associated protein 25 (SNAP-25) is a possible gene hypothesized to be associated with working memory deficits in ADHD, little is known about its specific impact on the hippocampus. The goal of the current study was to determine how variations in ADHD's SNAP-25 Mnll polymorphism (rs3746544) affect hippocampal functional connectivity (FC).

Methods

A total of 88 boys between the ages of 7 and 10 years were recruited for the study, including 60 patients with ADHD and 28 healthy controls (HCs). Data from resting-state functional magnetic resonance imaging (rs-fMRI) and clinical information were acquired and assessed. Two single nucleotide polymorphisms (SNP) in the SNAP-25 gene were genotyped, according to which the study's findings separated ADHD patients into two groups: TT homozygotes (TT = 35) and G-allele carriers (TG = 25).

Results

Based on the rs-fMRI data, the FC of the right hippocampus and left frontal gyrus was evaluated using group-based comparisons. The corresponding sensitivities and specificities were assessed. Following comparisons between the patient groups, different hippocampal FCs were identified. When compared to TT patients, children with TG had a lower FC between the right precuneus and the right hippocampus, and a higher FC between the right hippocampus and the left middle frontal gyrus.

Conclusion

The fundamental neurological pathways connecting the SNAP-25 Mnll polymorphism with ADHD via the FC of the hippocampus were newly revealed in this study. As a result, the hippocampal FC may further serve as an imaging biomarker for ADHD.

Common and distinct correlates of construction and elaboration of episodic-autobiographical memory: An ALE meta-analysis

The recollection of episodic-autobiographical memories (EAMs) entails a complex temporal dynamic, from initial "construction" to subsequent "elaboration" of memories. While there is consensus that EAM retrieval involves a distributed network of brain regions, it is still largely debated which regions specifically contribute to EAM construction and/or elaboration. To clarify this issue, we conducted an Activation Likelihood Estimation (ALE) meta-analysis based on the Preferred Reporting Items for Systematic-Reviews and Meta-Analyses (PRISMA) method. We found common recruitment of the left hippocampus and posterior cingulate cortex (PCC) during both phases. Additionally, EAM construction led to activations in the ventromedial prefrontal cortex, left angular gyrus (AG), right hippocampus, and precuneus, while the right inferior frontal gyrus was activated by EAM elaboration. Although most of these regions are distributed over the default mode network, the current findings highlight a differential contribution according to early (midline regions, left/right hippocampus, and left AG) versus later (left hippocampus, and PCC) recollection. Overall, these findings contribute to clarify the neural correlates that support the temporal dynamics of EAM recollection.

Sex differences in brain functional connectivity of hippocampus in mild cognitive impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's Disease (AD). Prior research shows that females are more impacted by MCI than males. On average females have a greater incidence rate of any dementia and current evidence suggests that they suffer greater cognitive deterioration than males in the same disease stage. Recent research has linked these sex differences to neuroimaging markers of brain pathology, such as hippocampal volumes. Specifically, the rate of hippocampal atrophy affects the progression of AD in females more than males. This study was designed to extend our understanding of the sex-related differences in the brain of participants with MCI. Specifically, we investigated the difference in the hippocampal connectivity to different areas of the brain. The Resting State fMRI and T2 MRI of cognitively normal individuals (n = 40, female = 20) and individuals with MCI (n = 40, female = 20) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the Functional Connectivity Toolbox (CONN). Our results demonstrate that connectivity of hippocampus to the precuneus cortex and brain stem was significantly stronger in males than in females. These results improve our current understanding of the role of hippocampus-precuneus cortex and hippocampus-brainstem connectivity in sex differences in MCI. Understanding the contribution of impaired functional connectivity sex differences may aid in the development of sex specific precision medicine to manipulate hippocampal-precuneus cortex and hippocampal-brainstem connectivity to decrease the progression of MCI to AD.

Altered Hippocampal Resting-state Functional Connectivity in Highly Superior Autobiographical Memory

Individuals with Highly Superior Autobiographical Memory (HSAM) provide the opportunity to investigate the neurobiological substrates of enhanced memory performance. While previous studies started to assess the neural correlates of memory retrieval in HSAM, here we assessed for the first time the intrinsic connectivity of a core memory region, the hippocampus, with the whole brain, in 8 HSAM subjects (HSAMs) and 21 controls during resting-state functional neuroimaging. We found in HSAMs vs. controls disrupted hippocampal resting-state functional connectivity (rsFC) with high-level control regions belonging to the saliency network (the anterior cingulate cortex and the left and right insulae), and to the ventral fronto-parietal attentional network (the temporo-parietal junction and the inferior frontal gyrus), also involved with salience detection. Conversely, HSAMs showed enhanced hippocampal rsFC with sensory regions along the fusiform gyrus and the inferior temporal cortex. This altered pattern of hippocampal rsFC might be interpreted as a reduced capability of HSAMs to discriminate and select salient information, with a subsequent increase in the probability to encode and consolidate sensory information irrespective of their task-relevancy. Ultimately, these findings provide evidence that HSAM might be paradoxically enabled by an altered hippocampal rsFC that bypasses regions involved with salience detection in favor of specialized sensory regions.

Aberrant Hippocampal Functional Connectivity Is Associated with Fornix White Matter Integrity in Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

Alzheimer's disease (AD) is the most common cause of dementia in older individuals, and amnestic mild cognitive impairment (aMCI) is currently considered the prodromal stage of AD. The hippocampus and fornix interact functionally and structurally, with the fornix being the major efferent white matter tract from the hippocampus.

OBJECTIVE

The main aim of this study was to examine the impairments present in subjects with AD or aMCI and the relationship of these impairments with the microstructure of the fornix and the functional connectivity (FC) and gray matter volume of the hippocampus.

METHODS

Forty-four AD, 34 aMCI, and 41 age- and gender-matched normal controls (NCs) underwent neuropsychological assessments and multimode MRI. We chose the bilateral hippocampi as the region of interest in which gray matter alterations and FC with the whole brain were assessed and the fornix body as the region of interest in which the microstructural integrity of the white matter was observed. We also evaluated the relationship among gray matter alterations, the abnormal FC of the hippocampus and the integrity of the fornix in AD/aMCIResults:Compared to the NC group, the AD and aMCI groups demonstrated decreased gray matter volume, reduced FC between the bilateral hippocampi and several brain regions in the default mode network and control network, and damaged integrity of the fornix body (decreased fractional anisotropy and increased diffusivity). We also found that left hippocampal FC with some regions, the integrity of the fornix body, and cognition ability were significantly correlated. Therefore, our findings suggest that damage to white matter integrity may partially explain the reduced resting-state FC of the hippocampus in AD and aMCI.

CONCLUSION

AD and aMCI are diseases of disconnectivity including not only functional but also structural disconnectivity. Damage to white matter integrity may partially explain the reduced resting-state FC in AD and aMCI. These findings have significant implications for diagnostics and modeling and provide insights for understanding the disconnection syndrome in AD.

Impaired hippocampal functional connectivity in patients with drug resistant, generalized tonic-clonic seizures

The aim of this study was to better understand the imaging features of drug-resistant epilepsy (DRE), especially in idiopathic generalized tonic-clonic seizure (GTCS), as well as to discover the associated mechanisms and functional connectivity (FC). A total of 31 idiopathic generalized epilepsy-GTCS patients and 17 healthy controls were enrolled. For each patient, resting-state functional MRI was performed. After a 12-month follow-up observation, patients were further divided into either drug-resistant (DR) or drug-sensitive (DS) groups. Compared to the DS group, DR patients had previously received more types of antiepileptic drugs and had taken more types of failed antiepileptic drugs. There were distinct FC changes toward the left thalamus, left putamen, left precuneus, and right precentral gyrus in the left hippocampus between DR and DS patients. FCs in the DR group largely decreased or remained unchanged, while DS patients exhibited compensatory enhancement. Disease duration was negatively correlated with FC between the left hippocampus and the left thalamus-putamen in patients with DRE. Further, DRE patients had an extremely high area under the curve (0.978) and a cut-off FC between the left hippocampus and thalamus-putamen of 0.282. Together, hippocampal FCs in patients with DR GTCS were impaired and time-dependently correlated with disease duration. Hippocampal FCs in DS patients showed overall compensatory enhancement, which could be used as a sensitive and specific marker to identify and predict DR GTCS.

ABCA7 polymorphisms correlate with memory impairment and default mode network in patients with APOEε4-associated Alzheimer's disease

Background

Since both APOE and ABCA7 protein expression may independently reduce neuritic plaque burden and reorganize fibrillar amyloid burden-mediated disruption of functional connectivity in the default mode network, we aimed to investigate the effect of the APOE-ABCA7 interaction on default mode network in Alzheimer’s disease.

Methods

Two hundred and eighty-seven individuals with a diagnosis of typical Alzheimer’s disease were included in this study. Memory was characterized and compared between APOE-ε4+ carriers and APOE-ε4 non-carriers within ABCA7 rs3764650T allele homozygous carriers and ABCA7 rs3764650G allele carriers, respectively. Two-way analysis of variance was used to identify a significant interaction effect between APOE (APOE-ε4+ carriers versus APOE-ε4 non-carriers) and ABCA7 (ABCA7 rs3764650T allele homozygous versus ABCA7 rs3764650G allele carriers) on memory scores and functional connectivity in each default mode network subsystem.

Results

In ABCA7 rs3764650G allele carriers, APOE-ε4+ carriers had lower memory scores (t (159) = − 4.879; P < 0.001) compared to APOE-ε4 non-carriers, but APOE-ε4+ carriers and APOE-ε4 non-carriers did not have differences in memory (P > 0.05) within ABCA7 rs3764650T allele homozygous carriers. There was a significant APOE-ABCA7 interaction effect on the memory (F3, 283 = 4.755, P = 0.030). In the default mode network anchored by the entorhinal seed, the peak neural activity of the cluster that was significantly associated with APOE-ABCA7 interaction effects (P = 0.00002) was correlated with the memory (ρ = 0.129, P = 0.030).

Conclusions

Genetic-biological systems may impact disease presentation and therapy. Clarifying the effect of APOE-ABCA7 interactions on the default mode network and memory is critical to exploring the complex pathogenesis of Alzheimer’s disease and refining a potential therapy.

Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: A Resting-State fMRI Study

The hippocampus is generally reported as one of the regions most impacted by Alzheimer's disease (AD) and is closely associated with memory function and orientation. Undirected functional connectivity (FC) alterations occur in patients with mild cognitive impairment (MCI) and AD, and these alterations have been the subject of many studies. However, abnormal patterns of directed FC remain poorly understood. In this study, to identify changes in directed FC between the hippocampus and other brain regions, Granger causality analysis (GCA) based on voxels was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 29 AD, 65 MCI, and 30 normal control (NC) subjects. The results showed significant differences in the patterns of directed FC among the three groups. There were fewer brain regions showing changes in directed FC with the hippocampus in the MCI group than the NC group, and these regions were mainly located in the temporal lobe, frontal lobe, and cingulate cortex. However, regarding the abnormalities in directed FC in the AD group, the number of affected voxels was greater, the size of the clusters was larger, and the distribution was wider. Most of the abnormal connections were unidirectional and showed hemispheric asymmetry. In addition, we also investigated the correlations between the abnormal directional FCs and cognitive and clinical measurement scores in the three groups and found that some of them were significantly correlated. This study revealed abnormalities in the transmission and reception of information in the hippocampus of MCI and AD patients and offer insight into the neurophysiological mechanisms underlying MCI and AD.

Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results

MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.

A prospective study of focal brain atrophy, mobility and fitness

BACKGROUND

The parallel decline of mobility and cognition with ageing is explained in part by shared brain structural changes that are related to fitness. However, the temporal sequence between fitness, brain structural changes and mobility loss has not been fully evaluated.

METHODS

Participants were from the Baltimore Longitudinal Study of Aging, aged 60 or older, initially free of cognitive and mobility impairments, with repeated measures of fitness (400-m time), mobility (6-m gait speed) and neuroimaging markers over 4 years (n = 332). Neuroimaging markers included volumes of total brain, ventricles, frontal, parietal, temporal and subcortical motor areas, and corpus callosum. Autoregressive models were used to examine the temporal sequence of each brain volume with mobility and fitness, adjusted for age, sex, race, body mass index, height, education, intracranial volume and APOE ɛ4 status.

RESULTS

After adjustment, greater volumes of total brain and selected frontal, parietal and temporal areas, and corpus callosum were unidirectionally associated with future faster gait speed over and beyond cross-sectional and autoregressive associations. There were trends towards faster gait speed being associated with future greater hippocampus and precuneus. Higher fitness was unidirectionally associated with future greater parahippocampal gyrus and not with volumes in other areas. Smaller ventricle predicted future higher fitness.

CONCLUSION

Specific regional brain volumes predict future mobility impairment. Impaired mobility is a risk factor for future atrophy of hippocampus and precuneus. Maintaining fitness preserves parahippocampal gyrus volume. Findings provide new insight into the complex and bidirectional relationship between the parallel decline of mobility and cognition often observed in older persons.

Individual susceptibility to TMS affirms the precuneal role in meta-memory upon recollection

A recent virtual-lesion study using inhibitory repetitive transcranial magnetic stimulation (rTMS) confirmed the causal behavioral relevance of the precuneus in the evaluation of one's own memory performance (aka mnemonic metacognition). This study's goal is to elucidate how these TMS-induced neuromodulatory effects might relate to the neural correlates and be modulated by individual anatomical profiles in relation to meta-memory. In a within-subjects design, we assessed the impact of 20-min rTMS over the precuneus, compared to the vertex, across three magnetic resonance imaging (MRI) neuro-profiles on 18 healthy subjects during a memory versus a perceptual task. Task-based functional MRI revealed that BOLD signal magnitude in the precuneus is associated with variation in individual meta-memory efficiency. Moreover, individuals with higher resting-state functional connectivity (rs-fcMRI) between the precuneus and the hippocampus, or smaller gray matter volume in the stimulated precuneal region exhibit considerably higher vulnerability to the TMS effect. These effects were not observed in the perceptual domain. Thus, we provide compelling evidence in outlining a possible circuit encompassing the precuneus and its mnemonic midbrain neighbor the hippocampus at the service of realizing our meta-awareness during memory recollection of episodic details.

Medial Temporal Lobe Disconnection and Hyperexcitability Across Alzheimer's Disease Stages

 The posteromedial cortex (PMC) and medial temporal lobes (MTL) are two brain regions particularly vulnerable in Alzheimer’s disease (AD). We have reviewed the spatiotemporal patterns of amyloid-β and tau accumulation, local MTL functional alterations and MTL-PMC network reconfiguration, and propose a model to relate these elements to each other. Functional and structural MTL-PMC disconnection happen concomitant with amyloid-β plaques and neurofibrillary tau accumulation within these same regions. Ongoing disconnection is accompanied by dysfunctional intrinsic local MTL circuit hyperexcitability, which exacerbates across distinct clinical stages of AD. Our overarching model proposes a sequence of events relating the spatiotemporal patterns of amyloid-β and tau accumulation to MTL-PMC disconnection and local MTL hyperexcitability. We hypothesize that cortical PMC amyloid-β pathology induces long-range information processing deficits through functional and structural MTL-PMC dysconnectivity at early disease stages, which in turn drives local MTL circuit hyperexcitability. Intrinsic local MTL circuit hyperexcitability subsequently accelerates local age-related tau deposition, facilitating tau spread from the MTL to the PMC, eventually resulting in extensive structural degeneration of white and grey matter as the disease advances. We hope that the present model may inform future longitudinal studies needed to test the proposed sequence of events.

Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.

Executive and Language Subjective Cognitive Decline Complaints Discriminate Preclinical Alzheimer's Disease from Normal Aging

BACKGROUND

There is a need to specify the profile of subjective cognitive decline in preclinical Alzheimer's disease (preAD).

OBJECTIVES

To explore specific items of the Subjective Cognitive Decline Questionnaire (SCD-Q) that discriminate preAD from normal aging.

METHODS

68 cognitively normal older adults were classified as controls (n = 52) or preAD (n = 16) according to amyloid-β (Aβ) levels. An exploratory factor analysis and item analysis of the SCD-Q were performed. Informant reports of the SCD-Q were used to corroborate the findings of self-reports. One-year neuropsychological follow-up was available.

RESULTS

Four SCD-Q factors were extracted: EM-factor (episodic memory), A-factor (attention), O-factor (organization), and L-factor (language). PreAD reported a significantly higher decline in L-factor (F(1) = 6.49; p = 0.014) and A-factor (F(1) = 4.04; p = 0.049) compared to controls, and showed a higher frequency of perceived decline in SCD-Q items related with language and executive tasks (Sig-items.) Significant discriminative powers for Aβ-positivity were found for L-factor (AUC = 0.75; p = 0.003) and A-factor (AUC = 0.74; p = 0.004). Informants in the preAD group confirmed significantly higher scores in L-factor and Sig-items. A significant time×group interaction was found in the Semantic Fluency and Stroop tests, with the preAD group showing a decrease in performance at one-year.

CONCLUSIONS

Our results suggest that SCD-Q items related with language and executive decline may help in prediction algorithms to detect preAD. Validation in an independent population is needed.

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease.

Cerebral changes and cognitive impairment after an ischemic heart disease: a multimodal MRI study

Three to 6 months after an acute coronary syndrome (ACS), cognitive impairment is observed in more than 30 % of the patients, mainly in executive functioning. The aim of this study was to investigate, using multimodal MRI, cerebral anatomo-functional substratum of executive dysfunction. Thirty-three patients were recruited 4 ± 1 months after a first ACS. Executive functions were evaluated with the Trail-Making-Test-B (TMTB) at baseline (ie 4 ± 1 months after ACS) and 6 months later (ie 10 ± 1 months after ACS). Using both time-points, we identified 3 groups of patients according to normative data based on age, gender and education level: 15 'cognitively normal' patients without impairment at each follow-up, 10 'transient impaired' patients with an impairment only at baseline and 8 'impairing' patients with an impairment only at follow-up. We explored, in the whole-brain, the structural integrity using Voxel-Based Morphometry and Tract-Based Spatial Statistics and the resting-state functional connectivity using Network-Based Statistics. No structural difference was observed between impaired and cognitively normal patients. At the functional level, compared to the 'cognitively normal' group, the 'transient impaired' patients presented an increased functional connectivity in a network centered on middle-orbito-frontal regions, whereas the 'impairing' patients presented only a non-significant decrease of functional connectivity. Executive dysfunction in ACS patients is associated to functional but no structural characteristics, particularly to an increased functional connectivity in cognitive networks in transient impaired patients. Further studies with larger sample size are needed to confirm these results and to determine if these patients could be at higher risk for developing permanent cognitive disorders.

Multifaceted brain networks reconfiguration in disorders of consciousness uncovered by co-activation patterns

INTRODUCTION

Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness.

METHODS

Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames.

RESULTS

Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of "pathological" within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) "pathological" increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames CONCLUSION: CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc.

Different Hippocampus Functional Connectivity Patterns in Healthy Young Adults with Mutations of APP/Presenilin-1/2 and APOEε4

This study aims to explore the hippocampus-based functional connectivity patterns in young, healthy APP and/or presenilin-1/2 mutation carriers and APOE ε4 subjects. Seventy-eight healthy young adults (33 male, mean age 24.0 ± 2.2 years; 18 APP and/or presenilin1/2 mutation carriers [APP/presenilin-1/2 group], 30 APOE ε4 subjects [APOE ε4 group], and 30 subjects without the above-mentioned genes [control group]) underwent resting-state functional MR imaging and neuropsychological assessments. Bilateral hippocampus functional connectivity patterns were compared among three groups. The brain regions with statistical differences were then extracted, and correlation analyses were performed between Z values of the brain regions and neuropsychological results. Compared with control group, both APOE ε4 group and APP/presenilin-1/2 group showed increased functional connectivity in medial prefrontal cortex and precuneus for the seeds of bilateral hippocampi. The APOE ε4 group displayed increased functional connectivity from bilateral hippocampi to the left middle temporal gyrus compared with the control group. Moreover, compared with the APP/presenilin-1/2 group, the APOE ε4 group also had markedly increased functional connectivity in right hippocampus-left middle temporal gyrus. The Z values of right hippocampus-left middle temporal gyrus correlated with various neuropsychological results across all the subjects, as well as in APOE ε4 group. Young healthy adults carrying APOE ε4 and APP/presenilin-1/2 displayed different hippocampus functional connectivity patterns, which may underlie the discrepant mechanisms of gene-modulated cognitive dysfunction in Alzheimer's disease.

Diminished Posterior Precuneus Connectivity with the Default Mode Network Differentiates Normal Aging from Alzheimer's Disease

Both normal aging and Alzheimer's disease (AD) have been associated with a reduction in functional brain connectivity. It is unknown how connectivity patterns due to aging and AD compare. Here, we investigate functional brain connectivity in 12 young adults (mean age 22.8 ± 2.8), 12 older adults (mean age 73.1 ± 5.2) and 12 AD patients (mean age 74.0 ± 5.2; mean MMSE 22.3 ± 2.5). Participants were scanned during 6 different sessions with resting state functional magnetic resonance imaging (RS-fMRI), resulting in 72 scans per group. Voxelwise connectivity with 10 functional networks was compared between groups (p < 0.05, corrected). Normal aging was characterized by widespread decreases in connectivity with multiple brain networks, whereas AD only affected connectivity between the default mode network (DMN) and precuneus. The preponderance of effects was associated with regional gray matter volume. Our findings indicate that aging has a major effect on functional brain interactions throughout the entire brain, whereas AD is distinguished by additional diminished posterior DMN-precuneus coherence.

The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment

Objectives

Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer’s disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI).

Method

MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus.

Results

We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors.

Conclusions

Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI.

Key Points

• PCC functioning during episodic memory relates to hippocampal functioning in MCI.

• PCC functioning during episodic memory does not relate to hippocampal structure in MCI.

• Functional network changes are an important predictor of PCC functioning in MCI.

Electronic supplementary material

The online version of this article (doi:10.1007/s00330-017-4768-1) contains supplementary material, which is available to authorized users.

Reduced functional connectivity between bilateral precuneus and contralateral parahippocampus in schizotypal personality disorder

BACKGROUND

Schizotypal personality disorder (SPD) is linked to schizophrenia in terms of shared genetics, biological markers and phenomenological characteristics. In the current study, we aimed to determine whether the previously reported altered functional connectivity (FC) with precuneus in patients with schizophrenia could be extended to individuals with SPD.

METHODS

Twenty subjects with SPD and 19 healthy controls were recruited from 4461 freshmen at a university in Shanghai and received a resting-state scan of MRI. All participants were evaluated by the Chinese version of Schizotypal Personality Questionnaire (SPQ) and the Chinese version of Symptom Checklist (SCL-90). The imaging data were analysed using the seed-based functional connectivity method.

RESULTS

Compared with the controls, SPD subjects exhibited reduced FC between bilateral precuneus and contralateral parahippocampus. In SPD group, SPQ total score was negatively correlated with FC between right precuneus and left parahippocampus (r = -0.603, p = 0.006); there was a negative trend between SPQ subscale score of suspiciousness and FC between left precuneus and right parahippocampus (r = -0.553, p = 0.014); and a positive trend was found between SPQ subscale score of odd or eccentric behaviour and FC between left precuneus and right superior temporal gyrus (r = 0.543, p = 0.016). As for the SCL-90 score, a similar negative trend was found between SCL-90 subscale score of suspiciousness and FC between right precuneus and left parahippocampus (r = -0.535, p = 0.018) in SPD group.

CONCLUSIONS

Our findings suggest that the decreased functional connectivity between precuneus and contralateral parahippocampus might play a key role in the pathophysiology of schizophrenia spectrum disorder.

The Primacy Effect in Amnestic Mild Cognitive Impairment: Associations with Hippocampal Functional Connectivity

Background: The “primacy effect,” i.e., increased memory recall for the first items of a series compared to the following items, is reduced in amnestic mild cognitive impairment (aMCI). Memory task-fMRI studies demonstrated that primacy recall is associated with higher activation of the hippocampus and temporo-parietal and frontal cortical regions in healthy subjects. Functional magnetic resonance imaging (fMRI) at resting state revealed that hippocampus functional connectivity (FC) with neocortical brain areas, including regions of the default mode network (DMN), is altered in aMCI. The present study aimed to investigate whether resting state fMRI FC between the hippocampus and cortical brain regions, especially the DMN, is associated with primacy recall performance in aMCI.

Methods: A number of 87 aMCI patients underwent resting state fMRI and verbal episodic memory assessment. FC between the left or right hippocampus, respectively, and all other voxels in gray matter was mapped voxel-wise and used in whole-brain regression analyses, testing whether FC values predicted delayed primacy recall score. The delayed primacy score was defined as the number of the first four words recalled on the California Verbal Learning Test. Additionally, a partial least squares (PLS) analysis was performed, using DMN regions as seeds to identify the association of their functional interactions with delayed primacy recall.

Results: Voxel-based analyses indicated that delayed primacy recall was mainly (positively) associated with higher FC between the left and right hippocampus. Additionally, significant associations were found for higher FC between the left hippocampus and bilateral temporal cortex, frontal cortical regions, and for higher FC between the right hippocampus and right temporal cortex, right frontal cortical regions, left medial frontal cortex and right amygdala (p < 0.01, uncorr.). PLS analysis revealed positive associations of delayed primacy recall with FC between regions of the DMN, including the left and right hippocampus, as well as middle cingulate cortex and thalamus (p < 0.04). In conclusion, in the light of decreased hippocampus function in aMCI, inter-hemispheric hippocampus FC and hippocampal FC with brain regions predominantly included in the DMN may contribute to residual primacy recall in aMCI.

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the extraction of hidden representations of fMRI volumes associated with tasks across multiple hidden layers. Our study may be beneficial to the automatic classification/diagnosis of neuropsychiatric and neurological diseases and prediction of disease severity and recovery in (pre-) clinical settings using fMRI volumes without requiring an estimation of activation patterns or ad hoc statistical evaluation.

The Inclusion of Functional Connectivity Information into fMRI-based Neurofeedback Improves Its Efficacy in the Reduction of Cigarette Cravings

Real-time fMRI (rtfMRI) neurofeedback (NF) facilitates volitional control over brain activity and the modulation of associated mental functions. The NF signals of traditional rtfMRI-NF studies predominantly reflect neuronal activity within ROIs. In this study, we describe a novel rtfMRI-NF approach that includes a functional connectivity (FC) component in the NF signal (FC-added rtfMRI-NF). We estimated the efficacy of the FC-added rtfMRI-NF method by applying it to nicotine-dependent heavy smokers in an effort to reduce cigarette craving. ACC and medial pFC as well as the posterior cingulate cortex and precuneus are associated with cigarette craving and were chosen as ROIs. Fourteen heavy smokers were randomly assigned to receive one of two types of NF: traditional activity-based rtfMRI-NF or FC-added rtfMRI-NF. Participants received rtfMRI-NF training during two separate visits after overnight smoking cessation, and cigarette craving score was assessed. The FC-added rtfMRI-NF resulted in greater neuronal activity and increased FC between the targeted ROIs than the traditional activity-based rtfMRI-NF and resulted in lower craving score. In the FC-added rtfMRI-NF condition, the average of neuronal activity and FC was tightly associated with craving score (Bonferroni-corrected p = .028). However, in the activity-based rtfMRI-NF condition, no association was detected (uncorrected p &gt; .081). Non-rtfMRI data analysis also showed enhanced neuronal activity and FC with FC-added NF than with activity-based NF. These results demonstrate that FC-added rtfMRI-NF facilitates greater volitional control over brain activity and connectivity and greater modulation of mental function than activity-based rtfMRI-NF.

Clinical effect of white matter network disruption related to amyloid and small vessel disease

BACKGROUND

We tested our hypothesis that the white matter network might mediate the effect of amyloid and small vessel disease (SVD) on cortical thickness and/or cognition.

METHODS

We prospectively recruited 232 patients with cognitive impairment. Amyloid was assessed using Pittsburgh compound B-PET. SVD was quantified as white matter hyperintensity volume and lacune number. The regional white matter network connectivity was measured as regional nodal efficiency by applying graph theoretical analysis to diffusion tensor imaging data. We measured cortical thickness and performed neuropsychological tests.

RESULTS

SVD burden was associated with decreased nodal efficiency in the bilateral frontal, lateral temporal, lateral parietal, and occipital regions. Path analyses showed that the frontal nodal efficiency mediated the effect of SVD on the frontal atrophy and frontal-executive dysfunction. The temporoparietal nodal efficiency mediated the effect of SVD on the temporoparietal atrophy and memory dysfunction. However, Pittsburgh compound B retention ratio affected cortical atrophy and cognitive impairment without being mediated by nodal efficiency.

CONCLUSIONS

We suggest that a disrupted white matter network mediates the effect of SVD, but not amyloid, on specific patterns of cortical atrophy and/or cognitive impairment. Therefore, our findings provide insight to better understand how amyloid and SVD burden can give rise to brain atrophy or cognitive impairment in specific patterns.

Altered Gray Matter Structural Covariance Networks in Early Stages of Alzheimer's Disease

Clinical symptoms observed in Alzheimer's disease (AD) patients may reflect variations within specific large-scale brain networks, modeling AD as a disconnection syndrome. The present magnetic resonance imaging study aims to compare the organization of gray matter structural covariance networks between 109 cognitively unimpaired controls (CTRL) and 109 AD patients positive to beta-amyloid at the early stages of the disease, using voxel-based morphometry. The default-mode network (DMN; medial temporal lobe subsystem) was less extended in AD patients in comparison with CTRL, with a significant decrease in the structural association between the entorhinal cortex and the medial prefrontal and the dorsolateral prefrontal cortices. The DMN (midline core subsystem) was also less extended in AD patients. Trends toward increased structural association were observed in the salience and executive control networks. The observed changes suggest that early disruptions in structural association between heteromodal association cortices and the entorhinal cortex could contribute to an isolation of the hippocampal formation, potentially giving rise to the clinical hallmark of AD, progressive memory impairment. It also provides critical support to the hypothesis that the reduced connectivity within the DMN in early AD is accompanied by an enhancement of connectivity in the salience and executive control networks.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.

Different patterns of brain activation in normal aging and Alzheimer's disease from cognitional sight: meta analysis using activation likelihood estimation

Alzheimer disease (AD) is a chronic neurological disease, frequently affecting cognitional functions. Recently, a large body of neuro-imaging studies have aimed at finding reliable biomarkers of AD for early diagnosis of disease in contrast with healthy elderlies. We intended to have a meta-analytical study on recent functional neuroimaging studies to find the relationship between cognition in AD patients and normal elderlies. A systematic search was conducted to collect functional neuroimaging studies such as positron emission therapy (PET) and functional magnetic resonance imaging (fMRI) in AD patients and healthy elderlies. The coordinates of regions related to cognition were meta-analyzed using the activation likelihood estimation (ALE) method and Sleuth software. P-value map at the false discovery rate (FDR) of P<0.05 thresholds and the clusters with a minimum size of 200 mm(3) were considered. Data were visualized with MANGO software. Forty-one articles that explored the areas activated during cognition in normal elderly subjects and AD patients were found. According to the findings, left middle frontal gyrus and left precuneus are the most activated areas in cognitional tasks in healthy elderlies and AD patients respectively. In normal elderly subjects and AD patients, comparison of ALE maps and reverse contrast showed that insula and left precuneus were the most activated areas in cognitional aspects respectively. With respect to unification of left precuneus activation in cognitional tasks, it seems that this point can be a hallmark in primary differentiation of AD and healthy individuals.

Functional connectivity of the hippocampus in temporal lobe epilepsy: feasibility of a task-regressed seed-based approach

OBJECTIVE

Resting-state functional connectivity (FC) has revealed marked network dysfunction in patients with temporal lobe epilepsy (TLE) compared to healthy controls. However, the nature and the location of these changes have not been fully elucidated nor confirmed by other methodologies. We assessed the presence of hippocampal FC changes in TLE based on the low frequency residuals of task-related functional magnetic resonance imaging data after the removal of task-related activation [i.e., task-regressed functional connectivity MRI (fcMRI)].

METHOD

We employed a novel, task-regressed approach to quantify hippocampal FC, and compare hippocampal FC in 17 patients with unilateral TLE (9 left) with 17 healthy controls.

RESULTS

Our results suggest widespread FC reductions in the mesial cortex associated with the default mode network (DMN), and some local FC increases in the lateral portions of the right hemisphere. We found more pronounced FC decreases in the left hemisphere than in the right, and these FC decreases were greatest in patients with left TLE. Moreover, the FC reductions observed between the hippocampus and posterior cingulate, inferior parietal, paracentral regions are in agreement with previous resting state studies.

CONCLUSIONS

Consistent with the existing literature, FC reductions in TLE appear widespread with prominent reductions in the medial portion of the DMN. Our data expand the literature by demonstrating that reductions in FC may be greatest in the left hemisphere and in patients with left TLE. Overall, our findings suggest that task-regressed FC is a viable alternative to resting state and that future studies may extract similar information on network connectivity from already existing datasets.

Different neural manifestations of two slow frequency bands in resting functional magnetic resonance imaging: a systemic survey at regional, interregional, and network levels

Temporal and spectral perspectives are two fundamental facets in deciphering fluctuating signals. In resting state, the dynamics of blood oxygen level-dependent (BOLD) signals recorded by functional magnetic resonance imaging (fMRI) have been proven to be strikingly informative (0.01-0.1 Hz). The distinction between slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) has been described, but the pertinent data have never been systematically investigated. This study used fMRI to measure spontaneous brain activity and to explore the different spectral characteristics of slow-4 and slow-5 at regional, interregional, and network levels, respectively assessed by regional homogeneity (ReHo) and mean amplitude of low-frequency fluctuation (mALFF), functional connectivity (FC) patterns, and graph theory. Results of paired t-tests supported/replicated recent research dividing low-frequency BOLD fluctuation into slow-4 and slow-5 for ReHo and mALFF. Interregional analyses showed that for brain regions reaching statistical significance, FC strengths at slow-4 were always weaker than those at slow-5. Community detection algorithm was applied to FC data and unveiled two modules sensitive to frequency effects: one comprised sensorimotor structure, and the other encompassed limbic/paralimbic system. Graph theoretical analysis verified that slow-4 and slow-5 differed in local segregation measures. Although the manifestation of frequency differences seemed complicated, the associated brain regions can be grossly categorized into limbic/paralimbic, midline, and sensorimotor systems. Our results suggest that future resting fMRI research addressing the three above systems either from neuropsychiatric or psychological perspectives may consider using spectrum-specific analytical strategies.

Lower hippocampal volume predicts decrements in lane control among drivers with amnestic mild cognitive impairment

OBJECTIVES

There are few methods to discern driving risks in patients with early dementia and mild cognitive impairment (MCI). We aimed to determine whether structural magnetic resonance imaging (MRI) of the hippocampus-a biomarker of probable Alzheimer pathology and a measure of disease severity in those affected--is linked to objective ratings of on-road driving performance in older adults with and without amnestic MCI.

METHODS

In all, 49 consensus-diagnosed participants from an Alzheimer's Disease Research Center (15 diagnosed with amnestic MCI and 34 demographically similar controls) underwent structural MRI and on-road driving assessments.

RESULTS

Mild atrophy of the left hippocampus was associated with less-than-optimal ratings in lane control but not with other discrete driving skills. Decrements in left hippocampal volume conferred higher risk for less-than-optimal lane control ratings in the patients with MCI (B = -1.63, standard error [SE] = .74, Wald = 4.85, P = .028), but not in controls (B = 0.13, SE = .415, Wald = 0.10, P = .752). The odds ratio and 95% confidence interval for below-optimal lane control in the MCI group was 4.41 (1.18-16.36), which was attenuated to 3.46 (0.88-13.60) after accounting for the contribution of left hippocampal volume.

CONCLUSION

These findings suggest that there may be a link between hippocampal atrophy and difficulties with lane control in persons with amnestic MCI. Further study appears warranted to better discern patterns of brain atrophy in MCI and Alzheimer disease and whether these could be early markers of clinically meaningful driving risk.