Inflection Point in Course of Mild Cognitive Impairment: Increased Functional Connectivity of Default Mode Network

Cognitive and cortical network alterations in pediatric temporal lobe space-occupying lesions: an fMRI study

Background

Temporal lobe mass lesions are the most common intracranial space-occupying lesions in children, among various brain lobes. The temporal lobe is critically involved in higher cognitive functions, and surgical interventions often risk causing damage to these functions. If necessary interventions and prehabilitation can be conducted preoperatively, it might be possible to achieve a larger extent of lesion resection with minimal cognitive impairment. However, research in this area has been relatively limited in the past. Our study aims to fill this gap.

Methods

We enrolled 15 children with temporal lobe mass lesions and 15 age- and gender-matched healthy children as controls. All participants underwent cognitive assessments and functional MRI scans. The cognitive testing data and functional MRI data were then analyzed and compared between the two groups.

Results

Our findings suggest that children with temporal lobe mass lesions primarily exhibit impairments in working memory and sustained attention. Multiple brain network indices were altered in the affected children, with the most prominent change being hyperactivation of the default mode network (DMN). This hyperactivation was correlated with cognitive impairments, indicating that the overactivation of the DMN might represent an inefficient compensatory mechanism within the brain's networks.

Conclusion

Compared to healthy children, those with temporal lobe mass lesions experience deficits in working memory and sustained attention, and the hyperactivation of the DMN may be the underlying network mechanism driving these cognitive impairments. Our research offers a unique and clinically valuable reference for future studies on preoperative interventions and prehabilitation in this population.

Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.

Functional connectivity density aberrance in type 2 diabetes mellitus with and without mild cognitive impairment

Purpose

The objective of this study was to investigate alterations in functional connectivity density (FCD) mapping and their impact on functional connectivity (FC) among individuals diagnosed with Type 2 diabetes mellitus (T2DM) across different cognitive states. Moreover, the study sought to explore the potential association between aberrant FCD/FC patterns and clinical or cognitive variables.

Methods

A total of 211 participants were recruited for this study, consisting of 75 healthy controls (HCs), 89 T2DM patients with normal cognitive function (DMCN), and 47 T2DM patients with mild cognitive impairment (DMCI). The study employed FCD analysis to pinpoint brain regions exhibiting significant FCD alterations. Subsequently, these regions showing abnormal FCD served as seeds for FC analysis. Exploratory partial correlations were conducted to explore the relationship between clinical biochemical indicators, neuropsychological test scores, and altered FCD or FC.

Results

The FCD analysis revealed an increased trend in global FCD (gFCD), local FCD (lFCD), and long-range FCD (lrFCD) within the bilateral supramarginal gyrus (SMG) among individuals with DMCN. Additionally, significant lFCD alterations were observed in the right inferior frontal gyrus and left precuneus when comparing DMCN to HCs and DMCI.

Conclusion

When comparing individuals with T2DM and healthy controls (HCs), it was revealed that DMCN exhibited significant improvements in FCD. This suggests that the brain may employ specific compensatory mechanisms to maintain normal cognitive function at this stage. Our findings provide a novel perspective on the neural mechanisms involved in cognitive decline associated with T2DM.

Deep Spatio-Temporal Attention-based Recurrent Network from Dynamic Adaptive Functional Connectivity for MCI Identification

Most existing methods of constructing dynamic functional connectivity (dFC) network obtain the connectivity strength via the sliding window correlation (SWC) method, which estimates the connectivity strength at each time segment, rather than at each time point, and thus is difficult to produce accurate dFC network due to the influence of the window type and window width. Furthermore, the deep learning methods may not capture the discriminative spatio-temporal information that is closely related to disease, thus impacting the performance of (mild cognitive impairment) MCI identification. In this paper, a novel spatio-temporal attention-based bidirectional gated recurrent unit (STA-BiGRU) network is proposed to extract inherent spatio-temporal information from a dynamic adaptive functional connectivity (dAFC) network for MCI diagnosis. Specifically, we adopt a group lasso-based Kalman filter algorithm to obtain the dAFC network with more accurate connectivity strength at each time step. Then a spatial attention module with self-attention and a temporal attention module with multiple temporal attention vectors are incorporated into the BiGRU network to extract more discriminative disease-related spatio-temporal information. Finally, the spatio-temporal regularizations are employed to better guide the attention learning of STA-BiGRU network to enhance the robustness of the deep network. Experimental results show that the proposed framework achieves mean accuracies of 90.2%, 90.0%, and 81.5%, respectively, for three MCI classification tasks. This study provides a more effective deep spatio-temporal attention-based recurrent network and obtains good performance and interpretability of deep learning for psychiatry diagnosis research.

Convergent functional changes of the episodic memory impairment in mild cognitive impairment: An ALE meta-analysis

Background

Mild cognitive impairment (MCI) is considered to be an intermediate stage between normal aging and Alzheimer's disease (AD). The earliest and most common symptom of MCI is impaired episodic memory. When episodic memory is impaired in MCI patients, specific functional changes occur in related brain areas. However, there is currently a lack of a unified conclusion on this change. Therefore, the purpose of this meta-analysis is to find MRI-specific functional changes in episodic memory in MCI patients.

Methods

Based on three commonly used indicators of brain function: functional connectivity (FC), the amplitude of low-frequency fluctuation /fractional amplitude of low-frequency fluctuation (ALFF/fALFF), and regional homogeneity (ReHo), we systematically searched PubMed, Web of Science and Ovid related literature and conducted the strict screening. Then we use the activation likelihood estimation (ALE) algorithm to perform the coordinate-based meta-analysis.

Results

Through strict screening, this meta-analysis finally included 21 related functional neuroimaging research articles. The final result displays that functional changes of episodic memory in MCI patients are mainly located in the parahippocampal gyrus, precuneus, posterior cingulate gyrus, cuneus, middle temporal gyrus, middle frontal gyrus, lingual gyrus, and thalamus.

Conclusions

There are specific functional changes in episodic memory brain regions in MCI patients, and the brain functional network can regulate episodic memory through these brain regions. And these specific changes can assist in the early diagnosis of MCI, providing new ideas and directions for early identification and intervention in the process of MCI.

Convergent Functional Changes of Default Mode Network in Mild Cognitive Impairment Using Activation Likelihood Estimation

Background: Mild cognitive impairment (MCI) represents a transitional state between normal aging and dementia disorders, especially Alzheimer's disease (AD). The disruption of the default mode network (DMN) is often considered to be a potential biomarker for the progression from MCI to AD. The purpose of this study was to assess MRI-specific changes of DMN in MCI patients by elucidating the convergence of brain regions with abnormal DMN function.

Methods: We systematically searched PubMed, Ovid, and Web of science for relevant articles. We identified neuroimaging studies by using amplitude of low frequency fluctuation /fractional amplitude of low frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in MCI patients. Based on the activation likelihood estimation (ALE) algorithm, we carried out connectivity modeling of coordination-based meta-analysis and functional meta-analysis.

Results: In total, this meta-analysis includes 39 articles on functional neuroimaging studies. Using computer software analysis, we discovered that DMN changes in patients with MCI mainly occur in bilateral inferior frontal lobe, right medial frontal lobe, left inferior parietal lobe, bilateral precuneus, bilateral temporal lobe, and parahippocampal gyrus (PHG).

Conclusions: Herein, we confirmed the presence of DMN-specific damage in MCI, which is helpful in revealing pathology of MCI and further explore mechanisms of conversion from MCI to AD. Therefore, we provide a new specific target and direction for delaying conversion from MCI to AD.

Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease

OBJECTIVES

Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker.

METHODS

Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study. IVIM data were subjected to postprocessing, and parameters including the apparent diffusion coefficient (ADC), slow diffusion coefficient (D_s), fast diffusion coefficient (D_f), perfusion fraction (fp) and D_f*fp were calculated. The classification model was developed and confirmed with cross-validation (group A/B) using Support Vector Machine (SVM). Correlations between IVIM parameters and Mini-Mental State Examination (MMSE) scores in AD patients were investigated using partial correlation analysis.

RESULTS

Diffusion MRI revealed significant region-specific differences that aided in differentiating AD patients from controls. Among the analyzed regions and parameters, the D_f of the right precuneus (PreR) (ρ = 0.515; P = 0.006) and the left cerebellum (CL) (ρ = 0.429; P = 0.026) demonstrated significant associations with the cognitive function of AD patients. An area under the receiver operating characteristics curve (AUC) of 0.84 (95% CI: 0.66, 0.99) was calculated for the validation in dataset B after the prediction model was trained on dataset A. When the datasets were reversed, an AUC of 0.90 (95% CI: 0.75, 1.00) was calculated for the validation in dataset A, after the prediction model trained in dataset B.

CONCLUSION

IVIM imaging is a promising method for the classification of AD and NC subjects, and IVIM parameters of precuneus and cerebellum might be effective biomarker for the diagnosis of AD.

The Anterior-posterior Functional Connectivity Disconnection in the Elderly with Subjective Memory Impairment and Amnestic Mild Cognitive Impairment

BACKGROUND

Subjective Memory Impairment (SMI) may tremendously increase the risk of Alzheimer's Disease (AD). The full understanding of the neuromechanism of SMI will shed light on the early intervention of AD.

METHODS

In the current study, 23 Healthy Controls (HC), 22 SMI subjects and 24 amnestic Mild Cognitive Impairment (aMCI) subjects underwent the comprehensive neuropsychological assessment and the resting-state functional magnetic resonance imaging scan. The difference in the connectivity of the Default Mode Network (DMN) and Functional Connectivity (FC) from the Region of Interest (ROI) to the whole brain were compared, respectively.

RESULTS

The results showed that HC and SMI subjects had significantly higher connectivity in the region of the precuneus area compared to aMCI subjects. However, from this region to the whole brain, SMI and aMCI subjects had significant FC decrease in the right anterior cingulum, left superior frontal and left medial superior frontal gyrus compared to HC. In addition, this FC change was significantly correlated with the cognitive function decline in participants.

CONCLUSION

Our study indicated that SMI subjects had relatively intact DMN connectivity but impaired FC between the anterior and posterior brain. The findings suggest that long-distance FC is more vulnerable than the short ones in the people with SMI.

Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Background

Alzheimer's disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.

Method

We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.

Results

All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.

Conclusion

Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

Increased intrinsic default-mode network activity as a compensatory mechanism in aMCI: a resting-state functional connectivity MRI study

Numerous studies have investigated the differences in the mean functional connectivity (FC) strength between amnestic mild cognitive impairment (aMCI) patients and normal subjects using resting-state functional magnetic resonance imaging. However, whether the mean FC is increased, decreased or unchanged in aMCI patients compared to normal controls remains unclear. Two factors might lead to inconsistent results: the determination of regions of interest and the reliability of the FC.

We explored differences in FC and the degree centrality (Dc) constructed by the bootstrap method, between and within networks (default-mode network (DN), frontoparietal control network (CN), dorsal attention network (AN)), and resulting from a hierarchical-clustering algorithm.

The mean FC within the DN and CN was significantly increased (P < 0.05, uncorrected) in patients. Significant increases (P < 0.05, uncorrected) in the mean FC were found in patients between DN and CN and between DN and AN. Five pairs of FC (false discovery rate corrected) and the Dc of six regions (Bonferroni corrected) displayed a significant increase in patients. Lower cognitive ability was significantly associated with a greater increase in the Dc of the left superior temporal sulcus.

Our results demonstrate that the early dysfunctions in aMCI disease are mainly compensatory impairments.

Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia

Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.

L1CAM Beneficially Inhibits Histone Deacetylase 2 Expression under Conditions of Alzheimer's Disease

BACKGROUND

Cognitive capacities in Alzheimer's Disease (AD) are impaired by an epigenetic blockade mediated by histone deacetylase 2 (HDAC2), which prevents the transcription of genes that are important for synaptic plasticity.

OBJECTIVE

Investigation of the functional relationship between cell adhesion molecule L1 and HDAC2 in AD.

METHODS

Cultures of dissociated cortical and hippocampal neurons from wild-type or L1-deficient mice were treated with Aβ1-42 for 24 h. After removal of Aβ1-42 cells were treated with the recombinant L1 extracellular domain (rL1) for 24 h followed by immunohistochemistry, western blotting, and reverse transcription PCR to evaluate the interaction between L1 and HDAC2.

RESULTS

Aβ and HDAC2 protein levels were increased in APPSWE/L1+/- mutant brains compared to APPSWE mutant brains. Administration of the recombinant extracellular domain of L1 to cultured cortical and hippocampal neurons reduced HDAC2 mRNA and protein levels. In parallel, reduced phosphorylation levels of glucocorticoid receptor 1 (GR1), which is implicated in regulating HDAC2 levels, was observed in response to L1 administration. Application of a glucocorticoid receptor inhibitor reduced Aβ-induced GR1 phosphorylation and prevented the increase in HDAC2 levels. HDAC2 protein levels were increased in cultured cortical neurons from L1-deficient mice. This change could be reversed by the administration of the recombinant extracellular domain of L1.

CONCLUSION

Our results suggest that some functionally interdependent activities of L1 and HDAC2 contribute to ameliorating the phenotype of AD by GR1 dephosphorylation, which leads to reduced HDAC2 expression. The combined findings encourage further investigations on the beneficial effects of L1 in the treatment of AD.

Genetic Variants of PICALM rs541458 Modulate Brain Spontaneous Activity in Older Adults With Amnestic Mild Cognitive Impairment

Background: Phosphatidylinositol binding clathrin assembly protein (PICALM) rs541458 C allele has been identified and validated to be associated with a reduction of Alzheimer's disease (AD) risk. Nevertheless, the exact mechanisms through which the variant exert its disease-relevant association remain to be elucidated. This study is to determine whether PICALM rs541458 polymorphism modulates functional magnetic resonance imaging measured brain spontaneous activity in older adults with amnestic mild cognitive impairment (aMCI).

Methods: Thirty five aMCI patients and twenty six healthy controls (HC) were enrolled in this study. Each individual was genotyped for rs541458 and scanned with resting-state functional magnetic resonance imaging. Each group was divided into two subgroups (C carriers and TT genotype). Brain activity was measured with amplitude of low-frequency fluctuation (ALFF).

Results: The aMCI patients showed decreased ALFF in left inferior frontal gyrus, superior temporal gyrus and insula, while increased ALFF in right cuneus, calcarine, and bilateral posterior cingulate and precuneus. A significant interaction between diagnosis (aMCI vs. HC) and PICALM rs541458 genotype (C carriers vs. TT) on ALFF was observed mainly in the right frontal lobe, with aMCI C carriers and TT genotype in HC showing significantly lower ALFF than HC C carriers. While only negative correlation between ALFF and verbal fluency test was found in HC C carriers (r = −0.543, p = 0.030).

Conclusions: This study provided preliminary evidences that PICALM rs541458 variations may modulate the spontaneous brain activity in aMCI patients.

Enhanced functional connectivity of the default mode network (DMN) in patients with spleen deficiency syndrome: A resting-state fMRI study

Numerous studies had investigated the biological basis of spleen deficiency syndrome on gastrointestinal dysfunctions. However, little was known about neuropsychological mechanism of spleen deficiency syndrome. The default model network (DMN) plays an important role in cognitive processing. Our aim is to investigate the change of neuropsychological tests and DMN in patients with spleen deficiency syndrome.Sixteen patients and 12 healthy subjects underwent functional magnetic resonance imaging examination, and 15 patients with spleen deficiency syndrome and 6 healthy subjects take part in the two neuropsychological tests.Compared with healthy subjects, patients with spleen deficiency syndrome revealed significantly increased functional connectivity within DMN, and significantly higher in the scores of 2-FT (P = .002) and 3-FT (P = .014).Our findings suggest that patients with spleen deficiency syndrome are associated with abnormal functional connectivity of DMN and part of neuropsychological tests, which provide new evidence in neuroimaging to support the notion of TCM that the spleen stores Yi and domains thoughts.

Neural bases underlying the association between balanced time perspective and trait anxiety

The aims of present study were to investigate the association between balanced time perspective (BTP) and trait anxiety, and the neural substrates underlying this association using voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods. 140 college students (83 females) ranging in age from 17 to 25 years were assessed on deviation from the balanced time perspective (DBTP) and trait anxiety. Behavioral analyses found BTP could significantly predict trait anxiety after controlling age and gender. Whole-brain VBM analyses found that DBTP was positively correlated with gray matter volume (GMV) in the parahippocampal gyrus (PHG) and precuneus, while trait anxiety positively correlated with GMV in the PHG. Considering the overlapping region in the PHG, we further defined the overlapping region as the seed, and calculated seed-to-voxel-based functional connectivity in resting-state. RSFC results showed that DBTP was positively associated with the RSFC between the PHG and medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and precuneus, whereas negatively correlated with the RSFC between the PHG and cuneus. Trait anxiety was also positively correlated with the RSFC between the PHG and PCC while negatively correlated with the RSFC between the PHG and cuneus. Mediation analysis further found GMV in the overlapping PHG and PHG-PCC, PHG-cuneus functional connectivity played significantly mediating roles in the relation between DBTP and trait anxiety. In sum, our research suggests the structural features of the PHG and its connectivity with PCC and cuneus may be the neural bases underlying the association between BTP and trait anxiety.