Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

Neurophysiological changes associated with Alzheimer's disease (AD) begin decades before clinical symptoms emerge, during preclinical AD. Functional abnormalities in white matter (WM) at this preclinical stage remain largely unexplored. We obtained resting-state functional magnetic resonance imaging (rs-fMRI) data of 295 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and evaluated bundle-wise functional connectivity density (FCD) and fractional amplitude of low-frequency fluctuations (fALFF) across 46 bundles, which reflects the strength of synchronizations of BOLD dynamics between each WM bundle and whole-brain 200 GM parcels, and spontaneous neural activity of each WM bundle, respectively. To mitigate site/scanner effects on the metrics, ComBat harmonization was applied to the data. We then performed permutation tests (n=5,000) on each harmonized metric for each bundle to determine differences in FCD and fALFF in preclinical AD relative to controls, adjusting for sex, age, and education using multiple linear regression. Linear correlations of the metrics with the pathological biomarker beta-amyloid (Aβ) and cognitive scores (mPACC and ADAS11) were assessed using general linear models. Multiple comparisons were corrected via a false discovery rate (FDR). We found that preclinical AD patients had reduced FCD and fALFF in specific WM bundles, such as cingulate and hippocampal cingulum, compared to controls (FDR corrected p < 0.05), some of which were associated with poorer cognitive performance and greater Aβ accumulation (FDR corrected p < 0.05). This study, to the best of our knowledge, is the first to examine bundle-wise FCD and fALFF of WM in preclinical AD using a large-scale, multi-site, cross-sectional dataset, suggesting potential applications of these metrics for assessing preclinical AD with rs-fMRI.

The resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging modality to measure brain activity and helps in the diagnosis of various brain-related disorders. Given the 1/f power spectrum characteristic of brain dynamics, where the energy value is higher at a low frequency than high frequency, it is established that low-frequency oscillations (LFO) provide a better representation of the spontaneous neuronal activity of the brain. In this research, a combination of the amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) from the resting-state blood oxygen level-dependent (BOLD) signal in the classic band i.e., 0.01-0.1 Hz is used for the classification of Alzheimer's disease (AD) from normal controls (NC). A total of 60 subjects participated in this study consisting of 30 AD patients and 30 NC from Alzheimer's disease neuroimaging initiative (ADNI). The feature selection is performed using minimum-redundancy maximum-relevance (mRMR) and ReliefF algorithm due to the large dimension of rs-fMRI data to be fed to the machine learning (ML) classifier. The proposed AD classification method employing the fusion of ALFF and fALFF obtained the highest classification accuracy of 96.36%, indicating the good potential of the proposed method for the diagnosis of AD, as well as other neurological conditions.

PURPOSEChronic neuro‐inflammation is an important mechanism underlying normal aging and Alzheimer's disease (AD) that is thought to potentiate medial temporal lobe (MTL) atrophy and memory decline. Currently, there are not enough prognostic biomarkers that can track the development and progression of normal aging versus AD, as well as differing effects on the brain. Our study examined which pro‐ or anti‐inflammatory cerebrospinal fluid (CSF) biomarkers best predicted AD neuropathology over 24 months.METHODSBaseline mass spectrometry data from two hundred and eighty‐five adults was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our focus was on examining biomarkers directly associated with inflammatory processes. For our study, we utilized T1‐weighted medial temporal gray matter volumes at baseline and months 6, 12, and 24 after baseline. Statistical analyses were conducted using SPSS 23.0 software. We used linear mixed models as well as least significant differences (LSD) post‐hoc tests to analyze how variables differed by baseline diagnosis (normal aging, pre‐AD, AD) or pre‐AD patients that did or did not convert to AD. We used stepwise regression analyses to analyze which inflammatory biomarkers best predicted atrophy and memory decline over 24 months. We specifically focused on CSF levels of Neuronal Pentraxin 2 (NPTX2) and Chitinase‐3‐like‐protein‐1 (C3LP1), as they are the inflammatory biomarkers that significantly predicted MTL atrophy and memory decline. NPTX2 is a biomarker of synaptic plasticity. C3LP1 is a biomarker of microglial activation and chronic neuro‐inflammation. We also analyzed relative risk ratios for diagnosis with NPTX2 and C3LP1 with multinomial logistic regression analyses. Preliminary results for memory indicate that, regardless of baseline diagnosis, higher NPTX2 levels strongly predict higher memory scores by month 24 (Figure 1).RESULTSNPTX2 and C3LP1, respectively beneficial and adverse biomarkers of synaptic plasticity and microglial activation, loaded significantly. By month 24, higher baseline NPTX2 corresponded to less MTL atrophy [R2= .287] and less memory decline [R2= .560]. Conversely, higher C3LP1 modestly predicted more MTL atrophy [R2=.083].CONCLUSIONSOur study suggests that NPTX2 is a promising biomarker for predicting longitudinal changes in medial temporal volume, especially memory performance across the AD spectrum. Baseline NPTX2 performs better than C3LP1 in predicting MTL atrophy and memory decline over time. C3LP1 was a comparatively modest predictor of medial temporal atrophy and memory decline. For future directions, studies should investigate if clinical trials using anti‐inflammatory drugs/nutritional therapy increase or inhibit NPTX2 and/or C3LP1.Support or Funding InformationThis study was funded by Iowa State University and NIH K99 AG047282. Data collection and sharing for this project were funded by the ADNI (National Institutes of Health Grant U01‐AG‐024904) and Department of Defense ADNI (award number W81XWH‐12‐2‐0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the Alzheimer's Association and the Alzheimer's Drug Discovery Foundation. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private‐sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The data used in the preparation of this article were obtained from the ADNI database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

BackgroundEarly mild cognitive impairment (EMCI) represents a prodromal stage of dementia, and early detection is crucial for delaying dementia progression. However, accurately identifying its neuroimaging features remains challenging.ObjectiveTo comprehensively evaluate structural and functional neuroimaging changes in EMCI using multimodal magnetic resonance imaging (MRI) techniques.MethodsOne hundred and eleven participants were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI): 36 with cognitively normal (CN), 30 with EMCI, 32 with late mild cognitive impairment (LMCI), and 13 with Alzheimer's disease (AD). FreeSurfer software was employed to segment hippocampal and amygdala subregions. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity were processed using Data Processing & Analysis for Brain Imaging toolbox. Graph Theoretical Network Analysis toolbox was utilized to evaluate global functional network.ResultsThe volume of most hippocampal and amygdala subregions was decreased in AD group than those of EMCI group in structural MRI. Significant differences were found between EMCI and AD group in fALFF (right insula) and ReHo (bilateral caudate regions). EMCI group exhibited stronger functional connectivity between left hippocampus and right inferior temporal gyrus (compared to CN), left inferior temporal gyrus (compared to LMCI), and cerebellum crus 8 (compared to AD). EMCI group exhibited stronger connectivity between right hippocampus and left anterior cingulate gyrus compared to AD. Network metrics showed no significant differences among these groups, but all exhibited small-world properties.ConclusionsMultimodal MRI analysis revealed the neuroimaging characteristics of EMCI and promoted the understanding of the mechanisms underlying neuroimaging changes in EMCI.

White matter (WM) has traditionally been considered structurally important but functionally inert in fMRI research. However, growing evidence indicates that WM exhibits meaningful BOLD fluctuations and participates in functional connectivity. Here, we investigate alterations in WM functional network connectivity (FNC) across the Alzheimer's disease (AD) spectrum using resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; 415 cognitively normal (CN), 283 mild cognitive impairment (MCI), 91 AD). We applied a guided independent component analysis (ICA) approach based on a combined multiscale template including 202 intrinsic connectivity networks [ICNs; 97 WM, 105 gray matter (GM)] to estimate subject-specific timecourses and compute FNC. Group differences in WM-WM, GM-GM, and WM-GM functional network connectivity (AD-CN, AD-MCI, MCI-CN) were evaluated using two-sample t-tests on residual FNC values for age, sex, and mean framewise displacement. Multiple comparisons across edges were controlled using false discovery rate correction (q < 0.05), and effect sizes were quantified using Hedges' g. Results showed robust alterations in WM-WM and WM-GM connectivity in AD, particularly involving WM subcortical, frontal, sensorimotor, and occipitotemporal networks. Several WM-GM interactions with cerebellar and hippocampal GM networks were also disrupted, including reduced GM-cerebellar: WM-frontal coupling and increased GM-hippocampal: WM-frontal connectivity. Notably, MCI already showed WM-GM dysconnectivity relative to CN, suggesting that functional disruption of WM circuits emerges prior to overt dementia. These findings provide converging evidence that WM functional connectivity is both measurable and selectively altered across the AD continuum. Our findings support WM FNC as a candidate biomarker to GM-based measures for staging and monitoring AD. Together, these results position WM-GM dysconnectivity as an important systems-level signature of the AD continuum and support WM functional network connectivity as a promising complement to established GM-based measures for understanding disease progression.

ObjectivePostherpetic neuralgia (PHN), a refractory neuropathic pain following herpes zoster reactivation, lacks clear central mechanisms for emerging therapies like short-term spinal cord stimulation (stSCS). This longitudinal study used multimodal neuroimaging to examine the effects of 14-day stSCS on brain function and white matter microstructure in PHN patients, and to identify neural correlates of clinical improvements.MethodsIn this longitudinal, single-arm, pre-post study, 17 PHN patients received 14 days of continuous stSCS. Clinical outcomes including pain intensity (Numeric Rating Scale, NRS), anxiety and depression (Hospital Anxiety and Depression Scale, HADS), and sleep quality (Pittsburgh Sleep Quality Index, PSQI), were assessed pre-stSCS and 3 days post-stSCS. Resting-state functional MRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) data were acquired at both time points. Longitudinal changes in amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF) were analyzed, alongside white matter integrity via TBSS and ROI analysis of key tracts.ResultsPost-stSCS, significant improvements occurred in all clinical outcomes (Wilcoxon signed-rank, all p < 0.001). Neuroimaging showed no DTI microstructural changes but significant fALFF increases in regions including the dorsal striatum. Notably, right medial orbitofrontal cortex (mOFC) fALFF increases correlated with NRS reductions (Spearman’s r = 0.71, FDR-corrected p = 0.036). Baseline cingulum integrity (lower FA, higher MD/RD) predicted greater striatal fALFF changes (r = ±0.75, FDR-corrected p < 0.02).ConclusionThese findings suggest that stSCS’s early clinical benefits in PHN are mediated by rapid functional reorganization rather than immediate microstructural changes. This reorganization appears prominent within fronto-striatal circuits: specifically, mOFC functional changes correlate with analgesia, while baseline cingulum integrity predicts subsequent striatal plasticity. This provides initial mechanistic insights into stSCS and suggest that baseline brain structure could be explored as a potential biomarker for treatment response, warranting validation in larger, controlled cohorts.

Alzheimer disease (AD) leads to cognitive decline and alters functional connectivity (FC) in key brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) assesses these changes using static-FC for overall correlation and dynamic-FC for temporal variability. In AD, there is altered FC compared to normal conditions. The present study investigates possible region-specific functional abnormalities occurring longitudinally over 1 year. Our aim is to evaluate the potential usefulness of the static and dynamic approaches in identifying biomarkers of AD progression. The study involved 15 AD and 20 healthy participants from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) database, tracked over 2 visits within 1 year. Using constrained-independent component analysis, we assessed FC changes across 80-regions of interest in AD over the year, examining both static and dynamic conditions. The average regional FC decreased in AD compared to healthy subjects at baseline and after 1 year. The dynamic condition identifies similarities with a few additional changes in the FC compared to the static condition. In both analyses, the baseline assessment revealed reduced connectivity between the following regions: right-middle-occipital and left-superior-occipital, left-hippocampus and right-postcentral, left-lingual and left-fusiform, and precuneus and left-thalamus. Additionally, increased connectivity was found between the left-superior-occipital and precuneus regions. In the 1-year AD assessment, increased connectivity was noted between the right-superior-temporal-pole and right-insular, right-hippocampus and left-caudate, right-middle-occipital and right-superior-temporal-pole, and posterior-cingulate-cortex and middle-temporal-pole regions. Significant changes were observed at baseline in the frontal, occipital, and core basal-ganglia regions, progressing towards the temporal lobe and subcortical regions in the following year. After 1 year, we observed the aforementioned region-specific neurological differences in AD, significantly aiding diagnosis and disease tracking.

Over 1% of the world population suffer from schizophrenia, which is a mental and hereditary disorder. Diagnosis of this disorder is now based mainly on the clinical symptoms. As the first study in Iran, we used magnetic resonance imaging (MRI) technique to study biomarkers of schizophrenia that are detectable in resting state functional MRI (fMRI). In this study, fractional Amplitude of Low-Frequency Fluctuations (fALFF) of BOLD signals, as the reflection of neuronal oscillation and synchrony of activation, were investigated in two groups of subjects including 12 schizophrenia patients and 12 healthy controls. The results showed abnormal increasing in fALFF of specific brain regions including parts of cerebellum, superior temporal gyrus, middle temporal gyrus and brainstem, and fALFF reduction in parts of frontal lobe, lingual gyrus and precuneus in schizophrenia subjects compared to the healthy control group (p-value<0.05). Also, fALFF reduction is observed in some parts of Default Mode Network (cingulate gyrus, anterior frontal lobe and superior frontal lobe). These results can help psychiatrists and neuroscientists to understand this disorder deeply and find appropriate treatments.

Mental disorders, especially schizophrenia, still pose a great challenge for diagnosis in early stages. Recently, computer-aided diagnosis techniques based on resting-state functional magnetic resonance imaging (Rs-fMRI) have been developed to tackle this challenge. In this work, we investigate different decision-level and feature-level fusion schemes for discriminating between schizophrenic and normal subjects. Four types of fMRI features are investigated, namely the regional homogeneity, voxel-mirrored homotopic connectivity, fractional amplitude of low-frequency fluctuations and amplitude of low-frequency fluctuations. Data denoising and preprocessing were first applied, followed by the feature extraction module. Four different feature selection algorithms were applied, and the best discriminative features were selected using the algorithm of feature selection via concave minimization (FSV). Support vector machine classifiers were trained and tested on the COBRE dataset formed of 70 schizophrenic subjects and 70 healthy subjects. The decision-level fusion method outperformed the single-feature-type approaches and achieved a 97.85% accuracy, a 98.33% sensitivity, a 96.83% specificity. Moreover, feature-fusion scheme resulted in a 98.57% accuracy, a 99.71% sensitivity, a 97.66% specificity, and an area under the ROC curve of 0.9984. In general, decision-level and feature-level fusion schemes boosted the performance of schizophrenia detectors based on fMRI features.

BackgroundWe tested whether inflammation indexed by soluble tumor necrosis factor receptor-1 (sTNFR1) is related to cognitive decline. We examined serum sTNFR1 with cognition in the Health and Retirement Study (HRS) and cerebrospinal fluid (CSF) sTNFR1 with tau pathology and magnetic resonance imaging (MRI)–based atrophy in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Finally, we used Mendelian randomization (MR) to assess associations between genetically proxied sTNFR1 and regional brain volumes.MethodsData were from HRS (2016–2020; N = 6028) and ADNI (N = 287). In HRS, serum sTNFR1 was log-transformed (quartiles); in ADNI, CSF sTNFR1 was analyzed. Global cognition included word recall, serial 7 s, and counting backwards. In ADNI, cognition was measured by the Clinical Dementia Rating–Sum of Boxes (CDR-SB); CSF total tau/phosphorylated tau and longitudinal MRI regional volumes were analyzed. Associations were estimated with linear and linear mixed-effects models adjusted for demographic, clinical, and genetic covariates including apolipoprotein E ε4 (APOE ε4). Incident mild cognitive impairment (MCI)/dementia was modeled with cause-specific Cox and Fine–Gray models. Incremental prediction used optimism-corrected change in area under the curve (AUC; ΔAUC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), calibration, and decision curve analysis. MR used genome-wide association study (GWAS) statistics to test effects of genetically proxied sTNFR1 on MRI-derived regional volumes.ResultsIn HRS (follow-up 4 years), higher serum sTNFR1 was associated with lower baseline cognition and faster decline in global cognition (β = − 0.16/year). Higher sTNFR1 predicted MCI/dementia (Cox HR ≈ 1.17; Fine–Gray sHR ≈ 1.14); among cognitively normal individuals, risk was elevated (OR = 1.30; 95% CI, 1.03–1.63). Adding sTNFR1 to 2- and 4-year prediction models conferred small discrimination gains after internal validation (ΔAUC ≤ 0.003) and minimal or inconsistent net clinical benefit. In ADNI, higher CSF sTNFR1 was associated with greater CSF total tau and phosphorylated tau, and predicted accelerated caudate atrophy. Exploratory MR suggested a nominal association with reduced right inferior temporal volume, limited by instruments.ConclusionssTNFR1 is associated with cognitive decline and tau-related selective neurodegeneration, but provides limited incremental predictive value beyond established risk factors; external validation and replication are warranted.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12916-026-04623-3.

Although the combination antiretroviral treatment (cART) has considerably lowered the risk of HIV associated dementia (HAD), the incidence of neurocognitive impairments (NCI) has not decreased likely due to the insidious and slow progressive nature of HIV infection. Recent studies showed that the resting-state functional magnetic resonance imaging (rs-fMRI) is a prominent technique in helping the non-invasive analysis of neucognitive impairment. Our study is to explore the neuroimaging characteristics among people living with HIV (PLWH) with or without NCI in terms of cerebral regional and neural network by rs-fMRI, based on the hypothesis that HIV patients with and without NCI have independent brain imaging characteristics. 33 PLWH with NCI and 33 PLWH without NCI, recruited from the Cohort of HIV-infected associated Chronic Diseases and Health Outcomes, Shanghai, China (CHCDO) which was established in 2018, were categorized into the HIV-NCI and HIV-control groups, respectively, based on Mini-Mental State Examination (MMSE) results. The two groups were matched in terms of sex, education and age. Resting-state fMRI data were collected from all participants to analyze the fraction amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) to assess regional and neural network alterations in the brain. Correlations between fALFF/FC values in specific brain regions and clinical characteristics were also examined. The results showed increased fALFF values in the bilateral calcarine gyrus, bilateral superior occipital gyrus, left middle occipital gyrus, and left cuneus in the HIV-NCI group compared to the HIV-control group. Additionally, increased FC values were observed between the right superior occipital gyrus and right olfactory cortex, bilateral gyrus rectus, and right orbital part of the middle frontal gyrus in the HIV-NCI group. Conversely, decreased FC values were found between the left hippocampus and bilateral medial prefrontal gyrus, as well as bilateral superior frontal gyrus. The study concluded that abnormal spontaneous activity in PLWH with NCI primarily occurred in the occipital cortex, while defects in brain networks were mostly associated with the prefrontal cortex. The observed changes in fALFF and FC in specific brain regions provide visual evidence to enhance our understanding of the central mechanisms underlying the development of cognitive impairment in HIV patients.

Plasma p-tau217 predicting brain-wide tau accumulation in preclinical AD

Objective: Diverse resting-state functional magnetic resonance imaging (rs-fMRI) studies showed that rs-fMRI might be able to reflect the earliest detrimental effect of cerebral beta-amyloid (Aβ) pathology. However, no previous studies specifically compared the predictive value of different rs-fMRI parameters in preclinical AD.Methods: A total of 106 cognitively normal adults (Aβ+ group = 66 and Aβ− group = 40) were included. Three different rs-fMRI parameter maps including functional connectivity (FC), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) were calculated. Receiver operating characteristic (ROC) curve analyses were utilized to compare classification performance of the three rs-fMRI parameters.Results: FC maps showed the best classifying performance in ROC curve analysis (AUC, 0.915, p < 0.001). Good but weaker performance was achieved by using ReHo maps (AUC, 0.836, p < 0.001) and fALFF maps (AUC, 0.804, p < 0.001). The brain regions showing the greatest discriminative power included the left angular gyrus for FC, left anterior cingulate for ReHo, and left middle frontal gyrus for fALFF. However, among the three measurements, ROI-based FC was the only measure showing group difference in voxel-wise analysis.Conclusion: Our results strengthen the idea that rs-fMRI might be sensitive to earlier changes in spontaneous brain activity and FC in response to cerebral Aβ retention. However, further longitudinal studies with larger sample sizes are needed to confirm their utility in predicting the risk of AD.

Introduction We recently showed that people with obstructive sleep apnea (OSA) have higher breathing rate (BR) variability (BRV) in population (Pal et al. 2021)1. Simultaneously, Lynch et al. 20202 had assessed impaired breathing in the resting state healthy Human Connectome Dataset, and identified 21 people in each of the three groups of breathing patterns: eupnea (clean), sighs (deep breath) and suspected apnea breathing pattern (burst). During resting state functional MRI recordings, increased amplitude of low frequency fluctuations (ALFF) were found within areas related to hyperarousal such as the midbrain and bilateral extra-nucleus, whereas decreased ALFF were found within areas associated with memory and attention involving the parietal and occipital lobule and others; furthermore, the altered ALFF was associated with sleep efficiency (Ran et al. 20173). Could altered breathing pattern reflect an altered spontaneous neuronal activity state measured by fractional ALFF (fALFF) and associated psycho-physiological markers of sleep quality? Our goal was to characterize BRV and fALFF from the resting state HCP data in the three breathing pattern groups identified in Lynch et al. 2020 along with their psychophysiological states including sleep quality measured by Pittsburg Sleep Quality Index (PSQI). Methods In the three groups (n=21 X 3), clean group (6 males) age (mean±std. dev) 29±4 years, deep breath group (5 males) 29±4 years, burst group (14 males) 30±4 years - we calculated BR, absolute BRV (Interquartile range of BR) and relative BRV% from 15 minutes of resting state respiration belt data; and, fALFF from the minimally preprocessed resting state fMRI data filtered at 0.1-0.08 Hz and smoothed. We correlated fALFF with BR, BRV. Additionally, in the three groups, we report the mean±stdev of PSQI, BMI, systolic and diastolic blood pressure (BP), self-reported anxiety, attention problem, aggression scores along with their correlations with the absolute BRV. Results Absolute BRV was lower in deep breath group 3±3 breaths per minute (bpm) compared to clean (4±2 bpm) and burst (4±3 bpm) groups. BR was also lower in deep breath 14±7 bpm and correlated with BRV at Pearson R = 0.57 (p&amp;lt;0.05), compared to 18±2 bpm, R = -0.51 (p&amp;lt;0.05) in clean group and 18±3 bpm, R = 0.2 in burst group. In the deep breath group, the relative change in BRV 24±14%, correlated less with absolute BRV R = 0.78 (p&amp;lt;0.05) compared to the clean 24±13%, R = 0.98 (p&amp;lt;0.05) and burst groups 24±18%, R = 0.97 (p&amp;lt;0.05) indicating some voluntary sighs in the deep breath group as also validated by visual data inspection. The sleep quality (lower PSQI better sleep quality) was best in the clean 4±2 points, compared to both deep breath 5±3 and burst groups 5±3. Improved psychophysiological state in the clean and deep breath group compared to burst group was indicated by the systolic BP (121±12, 121±13 and 128±14 mmHg), BMI (25±5, 26±5, 28±6 kg/m2), anxiety (5±5, 5±5, 7±7)/attention problems (5±3, 5±4, 7±4)/aggression (3±2, 3±3, 5±2) scores in clean, deep breath and burst groups respectively. Only in the burst group, higher BRV correlated with higher BMI (R = 0.5, p&amp;lt;0.05). fALFF correlated with BRV (p&amp;lt;0.05, FWE corrected), not BR in all three groups at the cerebrospinal fluid (CSF) ventricles. With BRV as co-variate, burst group showed higher fALFF activity (p&amp;lt;0.001, uncorrected) compared to both clean and deep breath groups at the visual and somatosensory regions. Additionally, fALFF at the central executive network (CEN) was higher (p&amp;lt;0.001, uncorrected) for both clean and deep breath groups compared to burst. Interestingly, the clean group as well as the burst group had higher right somatosensory fALFF activity compared to deep breath group, that corresponded to the lower BRV in deep breath group. Conclusion Higher fALFF activity of burst compared to clean and deep breath groups in the visual and somatosensory regions were associated with sleep deprivation states (Dai et al., 20124; Wang et al., 20165 , Ran et al. 20173). Higher CEN fALFF activity indicating better sleep and physiological states (Zeighami et al., 20216, Ran et al. 20173) were found in the clean and deep breath groups compared to burst. Higher right somatosensory fALFF activity in the clean and burst groups compared to deep breath indicating higher breathing related movements in the groups having higher BR and BRV (clean and burst) compared to the deep breath group. The fALFF results are consistent with the indication of breathing coupled hemodynamic and CSF low-frequency oscillations that indicate sleep/wakefulness states during resting state (Fultz 20197). Overall, our study supports that BRV could be a potential indicator of psychophysiology, and taking sighs or deep breaths could potentially improve some psycho-neuro-physiological states but not necessarily sleep quality. As indicated earlier by Lynch et al. 20202, males report more burst breathing pattern while females report more deep breathing pattern indicating the ability to take deep breaths may counteract potential sleep disordered breathing problems. Support (If Any) HL135562

PurposeTo determine whether functional connectivity (FC) mapping of nucleusbasalis of Meynert (NBM) cholinergic network (hereafter, NBM FC) couldprovide a biomarker of central cholinergic deficits with predictivepotential for response to cholinesterase inhibitor (ChEI) treatment.Materials and MethodsThe Alzheimer’s Disease Neuroimaging Initiative (ADNI) wasapproved by the institutional review boards of all participating sites.All participants and their representatives gave written informed consentprior to data collection. NBM FC was examined in 33 healthy controlparticipants, 102 patients with mild cognitive impairment (MCI), and 33patients with AD by using resting-state functional MRI data from theADNI database. NBM FC was compared between groups before and after 6months of ChEI treatment in MCI. Associations between baseline NBM FCand baseline cognitive performance as well as cognitive outcomes aftertreatment were investigated.ResultsCompared with the healthy control group, NBM FC was decreased in patientswith untreated MCI and increased in patients with AD treated with ChEI(corrected P ˂ .05). Global cognition(Alzheimer’s Disease Assessment Scale-Cognitive subscale score)was associated with NBM FC (r = −0.349;P ˂ .001). NBM FC was higher 6 months afterChEI compared with before ChEI in treated MCI (correctedP ˂ .05), but did not change at 6 months inpatients with untreated MCI (corrected P ˂ .05).Baseline NBM FC in MCI strongly predicted cognitive outcomes 6 monthsafter ChEI (R2 =0.458; P = .001).ConclusionFunctional dissociation of the nucleus basalis of Meynert from a corticalnetwork may explain the cognitive deficits in dementia and allow for theselection of individuals who are more likely to respond tocholinesterase inhibitors at early disease stages.Published under a CC BY-NC-ND 4.0 license.Onlinesupplemental material is available for thisarticle.