Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI

Levetiracetam Increases Hippocampal Blood Flow in Alzheimer's Disease as Measured by Arterial Spin Labelling MRI

BACKGROUND

Patients with Alzheimer's disease (AD) have an increased risk of developing epileptiform discharges, which is associated with a more rapid rate of progression. This suggests that suppression of epileptiform activity could have clinical benefit in patients with AD.

OBJECTIVE

In the current study, we tested whether acute, intravenous administration of levetiracetam led to changes in brain perfusion as measured with arterial spin labeling MRI (ASL-MRI) in AD.

METHODS

We conducted a double-blind, within-subject crossover design study in which participants with mild AD (n = 9) received placebo, 2.5 mg/kg, and 7.5 mg/kg of LEV intravenously in a random order in three sessions. Afterwards, the participants underwent ASL-MRI.

RESULTS

Analysis of relative cerebral blood flow (rCBF) between 2.5 mg of levetiracetam and placebo showed significant decreases in a cluster that included the posterior cingulate cortex, the precuneus, the posterior part of the cingulate gyrus, while increased cerebral blood flow was found in both temporal lobes involving the hippocampus.

CONCLUSION

Administration of 2.5 mg/kg of LEV in patients without any history of epilepsy leads to changes in rCBF in areas known to be affected in the early stages of AD. These areas may be the focus of the epileptiform activity. Larger studies are needed to confirm the current findings.

Relationship Between Prediction Accuracy and Feature Importance Reliability: an Empirical and Theoretical Study

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

A Survey on Brain Effective Connectivity Network Learning

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.

Low neighborhood deprivation buffers against hippocampal neurodegeneration, white matter hyperintensities, and poorer cognition

There is increasing recognition that socioeconomic inequalities contribute to disparities in brain and cognitive health in older adults. However, whether neighborhood socioeconomic status (SES) buffers individuals with low individual SES against neurodegeneration, cerebrovascular disease, and poorer cognitive function is not well understood. Here, we evaluated whether neighborhood deprivation (Townsend deprivation index) interacted with individual SES (composite household income and education levels) on hippocampus volume, regional cortical thickness, white matter hyperintensities, and cognition in 19,638 individuals (mean age = 54.8) from the UK Biobank. We found that individuals with low individual SES had the smallest hippocampal volumes, greatest white matter hyperintensity burden, and poorest cognition if they were living in high deprivation neighborhoods but that these deleterious effects on brain and cognitive function were attenuated if they were living in low deprivation neighborhoods (p for interactions < .05). While neighborhood deprivation did not interact with individual SES to influence regional cortical thickness, higher neighborhood deprivation was independently associated with lower cortical thickness in 16 regions (false discovery rate q < .05). Across multiple brain indices and cognitive function analyses, we found converging evidence suggesting that low neighborhood deprivation may have a neuroprotective effect against neurodegeneration, cerebrovascular pathology, and cognitive impairment, particularly in vulnerable individuals with low household income and education levels.

Source space connectomics of neurodegeneration: One-metric approach does not fit all

Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.

Subacute changes in brain functional network connectivity after nocturnal sodium oxybate intake are associated with anterior cingulate GABA

Sodium oxybate (γ-hydroxybutyrate, GHB) is an endogenous GHB/GABAB receptor agonist, clinically used to promote slow-wave sleep and reduce next-day sleepiness in disorders such as narcolepsy and fibromyalgia. The neurobiological signature of these unique therapeutic effects remains elusive. Promising current neuropsychopharmacological approaches to understand the neural underpinnings of specific drug effects address cerebral resting-state functional connectivity (rsFC) patterns and neurometabolic alterations. Hence, we performed a placebo-controlled, double-blind, randomized, cross-over pharmacological magnetic resonance imaging study with a nocturnal administration of GHB, combined with magnetic resonance spectroscopy of GABA and glutamate in the anterior cingulate cortex (ACC). In sum, 16 healthy male volunteers received 50 mg/kg GHB p.o. or placebo at 02:30 a.m. to maximize deep sleep enhancement and multi-modal brain imaging was performed at 09:00 a.m. of the following morning. Independent component analysis of whole-brain rsFC revealed a significant increase of rsFC between the salience network (SN) and the right central executive network (rCEN) after GHB intake compared with placebo. This SN-rCEN coupling was significantly associated with changes in GABA levels in the ACC (pall &lt; 0.05). The observed neural pattern is compatible with a functional switch to a more extrinsic brain state, which may serve as a neurobiological signature of the wake-promoting effects of GHB.

Identification of Homogeneous Subgroups from Resting-State fMRI Data

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.

Genotypic effects of APOE-ε4 on resting-state connectivity in cognitively intact individuals support functional brain compensation

The investigation of resting-state functional connectivity (rsFC) in asymptomatic individuals at genetic risk for Alzheimer's disease (AD) enables discovering the earliest brain alterations in preclinical stages of the disease. The APOE-ε4 variant is the major genetic risk factor for AD, and previous studies have reported rsFC abnormalities in carriers of the ε4 allele. Yet, no study has assessed APOE-ε4 gene-dose effects on rsFC measures, and only a few studies included measures of cognitive performance to aid a clinical interpretation. We assessed the impact of APOE-ε4 on rsFC in a sample of 429 cognitively unimpaired individuals hosting a high number of ε4 homozygotes (n = 58), which enabled testing different models of genetic penetrance. We used independent component analysis and found a reduced rsFC as a function of the APOE-ε4 allelic load in the temporal default-mode and the medial temporal networks, while recessive effects were found in the extrastriate and limbic networks. Some of these results were replicated in a subsample with negative amyloid markers. Interaction with cognitive data suggests that such a network reorganization may support cognitive performance in the ε4-homozygotes. Our data indicate that APOE-ε4 shapes the functional architecture of the resting brain and favor the idea of a network-based functional compensation.

The characteristics of glucose metabolism and functional connectivity in posterior default network during nondemented aging: relationship with executive function performance

BACKGROUND

Understanding the characteristics of intrinsic connectivity networks (ICNs) in terms of both glucose metabolism and functional connectivity (FC) is important for revealing cognitive aging and neurodegeneration, but the relationships between these two aspects during aging has not been well established in older adults.

OBJECTIVE

This study is to assess the relationship between age-related glucose metabolism and FC in key ICNs, and their direct or indirect effects on cognitive deficits in older adults.

METHODS

We estimated the individual-level standard uptake value ratio (SUVr) and FC of eleven ICNs in 59 cognitively unimpaired older adults, then analyzed the associations of SUVr and FC of each ICN and their relationships with cognitive performance.

RESULTS

The results showed both the SUVr and FC in the posterior default mode network (pDMN) had a significant decline with age, and the association between them was also significant. Moreover, both decline of metabolism and FC in the pDMN were significantly correlated with executive function decline. Finally, mediation analysis revealed the glucose metabolism mediated the FC decline with age and FC mediated the executive function deficits.

CONCLUSIONS

Our findings indicated that covariance between glucose metabolism and FC in the pDMN is one of the main routes that contributes to age-related executive function decline.

EEG resting-state networks in Alzheimer's disease associated with clinical symptoms

Alzheimer's disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD's cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer's Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease

From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.

Resting-state functional connectivity in lifelong musicians

Background

It has been postulated that musicianship can lead to enhanced brain and cognitive reserve, but the neural mechanisms of this effect have been poorly understood. Lifelong professional musicianship in conjunction with novel brain imaging techniques offers a unique opportunity to examine brain network differences between musicians and matched controls.

Objective

In this study we aim to investigate how resting-state functional networks (FNs) manifest in lifelong active musicians. We will evaluate the FNs of lifelong musicians and matched healthy controls using resting-state functional magnetic resonance imaging.

Methods

We derive FNs using the data-driven independent component analysis approach and analyze the functional network connectivity (FNC) between the default mode (DMN), sensory-motor (SMN), visual (VSN), and auditory (AUN) networks. We examine whether the linear regressions between FNC and age are different between the musicians and the control group.

Results

The age trajectory of average FNC across all six pairs of FNs shows significant differences between musicians and controls. Musicians show an increase in average FNC with age while controls show a decrease (P = 0.013). When we evaluated each pair of FN, we note that in musicians FNC values increased with age in DMN-AUN, DMN-VSN, and SMN-VSN and in controls FNC values decreased with age in DMN-AUN, DMN-SMN, AUN-SMN, and SMN-VSN.

Conclusion

This result provides early evidence that lifelong musicianship may contribute to enhanced brain and cognitive reserve. Results of this study are preliminary and need to be replicated with a larger number of participants.

Blood-brain barrier opening of the default mode network in Alzheimer's disease with magnetic resonance-guided focused ultrasound

The blood-brain barrier (BBB) protects the brain but is also an important obstacle for the effective delivery of therapeutics in Alzheimer's disease and other neurodegenerative disorders. Transcranial magnetic resonance-guided focused ultrasound (MRgFUS) has been shown to reversibly disrupt the BBB. However, treatment of diffuse regions across the brain along with the effect on Alzheimer's disease relevant pathology need to be better characterized. This study is an open-labelled single-arm trial (NCT04118764) to investigate the feasibility of modulating BBB permeability in the default mode network and the impact on cognition, amyloid and tau pathology as well as BBB integrity. Nine participants [mean age 70.2 ± 7.2 years, mean Mini-Mental State Examination (MMSE) 21.9] underwent three biweekly procedures with follow-up visits up to 6 months. The BBB permeability of the bilateral hippocampi, anterior cingulate cortex and precuneus was transiently increased without grade 3 or higher adverse events. Participants did not experience worsening trajectory of cognitive decline (ADAS-cog11, MMSE). Whole brain vertex-based analysis of the 18F-florbetaben PET imaging demonstrated clusters of modest SUVR reduction in the right parahippocampal and inferior temporal lobe. However, CSF and blood biomarkers did not demonstrate any amelioration of Alzheimer's disease pathology (P-tau181, amyloid-β42/40 ratio), nor did it show persistent BBB dysfunction (plasma PDGFRbeta and CSF-to-plasma albumin ratio). This study provides neuroimaging and fluid biomarker data to characterize the safety profile of MRgFUS BBB modulation in neurodegeneration as a potential strategy for enhanced therapeutic delivery.

The Effects of Transcutaneous Vagus Nerve Stimulation on Functional Connectivity Within Semantic and Hippocampal Networks in Mild Cognitive Impairment

Better treatments are needed to improve cognition and brain health in people with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Transcutaneous vagus nerve stimulation (tVNS) may impact brain networks relevant to AD through multiple mechanisms including, but not limited to, projection to the locus coeruleus, the brain's primary source of norepinephrine, and reduction in inflammation. Neuropathological data suggest that the locus coeruleus may be an early site of tau pathology in AD. Thus, tVNS may modify the activity of networks that are impaired and progressively deteriorate in patients with MCI and AD. Fifty patients with MCI (28 women) confirmed via diagnostic consensus conference prior to MRI (sources of info: Montreal Cognitive Assessment Test (MOCA), Clinical Dementia Rating scale (CDR), Functional Activities Questionnaire (FAQ), Hopkins Verbal Learning Test - Revised (HVLT-R) and medical record review) underwent resting state functional magnetic resonance imaging (fMRI) on a Siemens 3 T scanner during tVNS (left tragus, n = 25) or sham control conditions (left ear lobe, n = 25). During unilateral left tVNS, compared with ear lobe stimulation, patients with MCI showed alterations in functional connectivity between regions of the brain that are important in semantic and salience functions including regions of the temporal and parietal lobes. Furthermore, connectivity from hippocampi to several cortical and subcortical clusters of ROIs also demonstrated change with tVNS compared with ear lobe stimulation. In conclusion, tVNS modified the activity of brain networks in which disruption correlates with deterioration in AD. These findings suggest afferent target engagement of tVNS, which carries implications for the development of noninvasive therapeutic intervention in the MCI population.

Symptom-associated alterations in functional connectivity in primary and secondary provoked vestibulodynia

ABSTRACT

Primary provoked vestibulodynia (PVD) is marked by the onset of symptoms at first provoking vulvar contact, whereas secondary PVD refers to symptom onset after some period of painless vulvar contact. Different pathophysiological processes are believed to be involved in the development and maintenance of primary PVD and secondary PVD. The primary aim of this study was to test the hypotheses that the resting state functional connectivity of the brain and brain stem regions differs between these subtypes. Deep clinical phenotyping and resting state brain imaging were obtained in a large sample of a women with primary PVD (n = 46), those with secondary PVD (n = 68), and healthy control women (n = 94). The general linear model was used to test for differences in region-to-region resting state functional connectivity and psychosocial and symptom assessments. Direct statistical comparisons by onset type indicated that women with secondary PVD have increased dorsal attention-somatomotor network connectivity, whereas women with primary PVD predominantly show increased intrinsic resting state connectivity within the brain stem and the default mode network. Furthermore, compared with women with primary PVD, those with secondary PVD reported greater incidence of early life sexual abuse, greater pain catastrophizing, greater 24-hour symptom unpleasantness, and less sexual satisfaction. The findings suggest that women with secondary PVD show greater evidence for central amplification of sensory signals, whereas women with primary PVD have alterations in brain stem circuitry responsible for the processing and modulation of ascending and descending peripheral signals.

Brain correlates of subjective cognitive complaints in COVID-19 survivors: A multimodal magnetic resonance imaging study

Cognitive impairment represents a leading residual symptom of COVID-19 infection, which lasts for months after the virus clearance. Up-to-date scientific reports documented a wide spectrum of brain changes in COVID-19 survivors following the illness's resolution, mainly related to neurological and neuropsychiatric consequences. Preliminary insights suggest abnormal brain metabolism, microstructure, and functionality as neural under-layer of post-acute cognitive dysfunction. While previous works focused on brain correlates of impaired cognition as objectively assessed, herein we investigated long-term neural correlates of subjective cognitive decline in a sample of 58 COVID-19 survivors with a multimodal imaging approach. Diffusion Tensor Imaging (DTI) analyses revealed widespread white matter disruption in the sub-group of cognitive complainers compared to the non-complainer one, as indexed by increased axial, radial, and mean diffusivity in several commissural, projection and associative fibres. Likewise, the Multivoxel Pattern Connectivity analysis (MVPA) revealed highly discriminant patterns of functional connectivity in resting-state among the two groups in the right frontal pole and in the middle temporal gyrus, suggestive of inefficient dynamic modulation of frontal brain activity and possible metacognitive dysfunction at rest. Beyond COVID-19 actual pathophysiological brain processes, our findings point toward brain connectome disruption conceivably translating into clinical post-COVID cognitive symptomatology. Our results could pave the way for a potential brain signature of cognitive complaints experienced by COVID-19 survivors, possibly leading to identify early therapeutic targets and thus mitigating its detrimental long-term impact on quality of life in the post-COVID-19 stages.

Neuroinflammation is independently associated with brain network dysfunction in Alzheimer's disease

Brain network dysfunction is increasingly recognised in Alzheimer's disease (AD). However, the causes of brain connectivity disruption are still poorly understood. Recently, neuroinflammation has been identified as an important factor in AD pathogenesis. Microglia participate in the construction and maintenance of healthy neuronal networks, but pro-inflammatory microglia can also damage these circuits. We hypothesised that microglial activation is independently associated with brain connectivity disruption in AD. We performed a cross-sectional multimodal imaging study and interrogated the relationship between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 42 participants (12 Aβ-positive MCI, 14 Aβ-positive AD and 16 Aβ-negative healthy controls) were recruited. Participants had ¹¹C-PBR28 and ¹⁸F-flutemetamol PET to quantify Aβ deposition and microglial activation, T1-weighted, diffusion tensor and resting-state functional MRI to assess structural network and functional network. ¹¹C-PBR28 uptake, structural network integrity and functional network orgnisation were compared across diagnostic groups and the relationship between neuroinflammation and brain network was tested in 26 Aβ-positive patients. Increased ¹¹C-PBR28 uptake, decreased FA, network small-worldness and local efficiency were observed in AD patients. Cortical ¹¹C-PBR28 uptake correlated negatively with structural integrity (standardised β = -0.375, p = 0.037) and network local efficiency (standardised β = -0.468, p < 0.001), independent of cortical thickness and Aβ deposition, while Aβ was not. Network structural integrity, small-worldness and local efficiency, and cortical thickness were positively associated with cognition. Our findings suggest cortical neuroinflammation coincide with structural and functional network disruption independent of Aβ and cortical atrophy. These findings link the brain connectivity change and pathological process in Alzheimer's disease, and suggest a pathway from neuroinflammation to systemic brain dysfunction.

Structural But Not Functional Connectivity Differences within Default Mode Network Indicate Conversion to Dementia

BACKGROUND

Malfunctioning of the default mode network (DMN) has been consistently related to mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, evidence on differences in this network between MCI converters (MCI-c) and non-converters (MCI-nc), which could mark progression to AD, is still inconsistent.

OBJECTIVE

To multimodally investigate the DMN in the AD continuum.

METHODS

We measured gray matter (GM) volume, white matter (WM) integrity, and functional connectivity (FC) at rest in healthy elderly controls, MCI-c, MCI-nc, and AD patients, matched on sociodemographic variables.

RESULTS

Significant differences between AD patients and controls were found in the structure of most of the regions of the DMN. MCI-c only differed from MCI-nc in GM volume of the left parahippocampus and bilateral hippocampi and middle frontal gyri, as well as in WM integrity of the parahippocampal cingulum connecting the left hippocampus and precuneus. We found significant correlations between integrity in some of those regions and global neuropsychological status, as well as an excellent discrimination ability between converters and non-converters for the sum of GM volume of left parahippocampus, bilateral hippocampi, and middle frontal gyri, and WM integrity of left parahippocampal cingulum. However, we found no significant differences in FC.

CONCLUSION

These results further support the relationship between abnormalities in the DMN and AD, and suggest that structural measures could be more accurate than resting-state estimates as markers of conversion from MCI to AD.

Harnessing the potential of machine learning and artificial intelligence for dementia research

Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.

Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli

The default mode network (DMN) is critical for self-referential mental processes, and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber photometry with functional magnetic resonance imaging (fMRI) and computational modeling to characterize dynamics of putative rat DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. Our analysis revealed neuronal activity changes in AI and DMN nodes preceding fMRI-derived DMN activations and cyclical transitions between brain network states. Furthermore, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate the neurophysiological foundations of the rodent DMN, its spatiotemporal dynamical properties, and modulation by salient stimuli, paving the way for future translational studies.

A Residual Marker of Cognitive Reserve Is Associated with Resting-State Intrinsic Functional Connectivity Along the Alzheimer's Disease Continuum

BACKGROUND

Cognitive reserve (CR) explains inter-individual differences in the impact of the neurodegenerative burden on cognitive functioning. A residual model was proposed to estimate CR more accurately than previous measures. However, associations between residual CR markers (CRM) and functional connectivity (FC) remain unexplored.

OBJECTIVE

To explore the associations between the CRM and intrinsic network connectivity (INC) in resting-state networks along the neuropathological-continuum of Alzheimer's disease (ADN).

METHODS

Three hundred eighteen participants from the DELCODE cohort were stratified using cerebrospinal fluid biomarkers according to the A(myloid-β)/T(au)/N(eurodegeneration) classification. CRM was calculated utilizing residuals obtained from a multilinear regression model predicting cognition from markers of disease burden. Using an independent component analysis in resting-state fMRI data, we measured INC of resting-state networks, i.e., default mode network (DMN), frontoparietal network (FPN), salience network (SAL), and dorsal attention network. The associations of INC with a composite memory score and CRM and the associations of CRM with the seed-to-voxel functional connectivity of memory-related were tested in general linear models.

RESULTS

CRM was positively associated with INC in the DMN in the entire cohort. The A+T+N+ group revealed an anti-correlation between the SAL and the DMN. Furthermore, CRM was positively associated with anti-correlation between memory-related regions in FPN and DMN in ADN and A+T/N+.

CONCLUSION

Our results provide evidence that INC is associated with CRM in ADN defined as participants with amyloid pathology with or without cognitive symptoms, suggesting that the neural correlates of CR are mirrored in network FC in resting-state.

Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective

With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.

A resting-state functional MRI study in patients with vestibular migraine during interictal period

To evaluate the spontaneous neuronal activities and the changes of brain functional network in patients with vestibular migraine (VM). Three groups including18 patients with VM, 21 patients with migraine without aura (MWoA) and 21 healthy controls (HCs) underwent the scanning of the resting-state fMRI. Covariance analysis and bonferroni multiple comparisons were used to obtain brain regions with significant differences in amplitude of low-frequency fluctuation (ALFF) values. Furthermore, the brain regions with the most significant differences of ALFF values were recognized as a region of interest (ROI) and functional connectivity (FC) analysis was performed in these regions. (1) ALFF: Compared with HCs, patients with VM showed significantly lower ALFF in the right putamen (P < 0.05), and significantly higher ALFF in the right lingual gyrus (P < 0.05). In addition, compared with MWoA patients, patients with VM showed significantly higher ALFF in the right lingual gyrus (P < 0.05). (2) Compared with HCs, VM patients showed significantly higher FC among the cerebellum, the left dorsolateral superior frontal gyrus and the right putamen (P < 0.05) but significantly lower FC among the left median cingulate, paracingulate gyri and the right putamen (P < 0.05). Compared with MWoA patients, VM patients showed significantly higher FC between the cerebellum and the right putamen (P < 0.05) but significantly lower FC among the left median cingulate, paracingulate gyri and the right putamen (P < 0.05). There are functional abnormalities in nociceptive, vestibular and visual cortex regions in patients with VM during the interictal period.

Network localization of transient global amnesia beyond the hippocampus

BACKGROUND

Transient global amnesia is common in the older adult, but the cause and mechanism remain unclear. Focal brain lesions allow for causal links between the lesion location and resulting symptoms, and we based on the reported TGA-causing lesions and used lesion network mapping to explore the causal neuroanatomical substrate of TGA.

METHODS

Fifty-one cases of transient global amnesias with DWI lesions from the literature were identified, and clinical data were extracted and analyzed. Next, we mapped each lesion volume onto a reference brain and computed the network of regions functionally connected to each lesion location using a large normative connectome dataset.

RESULTS

Lesions primarily occurred in the hippocampus, and in addition to the hippocampus, there are also other locations of TGA-causing lesions such as the cingulate gyrus, anterior thalamic nucleus (ATN), putamen, caudate nucleus, corpus callosum, fornix. More than 90% of TGA-causing lesions inside the hippocampus were functionally connected with the default mode network (DMN).

CONCLUSION

Structural abnormality in the hippocampus was the most consistently reported in TGA, and besides the hippocampus, lesions occurring at several other brain locations also could cause TGA. The DMN may also be involved in the pathophysiology of TGA. According to the clinical and neuroimaging characteristics, TGA may be a syndrome with multiple causes and cannot be treated simply as a subtype of TIA.

Magnetic resonance-guided focused ultrasound thalamotomy restored distinctive resting-state networks in patients with essential tremor

OBJECTIVE

Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy ameliorates symptoms in patients with essential tremor (ET). How this treatment affects canonical brain networks has not been elucidated. The purpose of this study was to clarify changes of brain networks after MRgFUS thalamotomy in ET patients by analyzing resting-state networks (RSNs).

METHODS

Fifteen patients with ET were included in this study. Left MRgFUS thalamotomy was performed in all cases, and MR images, including resting-state functional MRI (rsfMRI), were taken before and after surgery. MR images of 15 age- and sex-matched healthy controls (HCs) were also used for analysis. Using rsfMRI data, canonical RSNs were extracted by performing dual regression analysis, and the functional connectivity (FC) within respective networks was compared among pre-MRgFUS patients, post-MRgFUS patients, and HCs. The severity of tremor was evaluated using the Clinical Rating Scale for Tremor (CRST) score pre- and postoperatively, and its correlation with RSNs was examined.

RESULTS

Preoperatively, ET patients showed a significant decrease in FC in the sensorimotor network (SMN), primary visual network (VN), and visuospatial network (VSN) compared with HCs. The decrease in FC in the SMN correlated with the severity of tremor. After MRgFUS thalamotomy, ET patients still exhibited a significant decrease in FC in a small area of the SMN, but they exhibited an increase in the cerebellar network (CN). In comparison between pre- and post-MRgFUS patients, the FC in the SMN and the VSN significantly increased after treatment. Quantitative evaluation of the FCs in these three groups showed that the SMN and VSN increased postoperatively and demonstrated a trend toward those of HCs.

CONCLUSIONS

The SMN and CN, which are considered to be associated with the cerebello-thalamo-cortical loop, exhibited increased connectivity after MRgFUS thalamotomy. In addition, the FC of the visual network, which declined in ET patients compared with HCs, tended to normalize postoperatively. This could be related to the hypothesis that visual feedback is involved in tremor severity in ET patients. Overall, the analysis of the RSNs by rsfMRI reflected the pathophysiology with the intervention of MRgFUS thalamotomy in ET patients and demonstrated a possibility of a biomarker for successful treatment.

Reorganization of resting-state brain network functional connectivity across human brain developmental stages

Cognitive brain aging can either be healthy or degenerative in nature. Multiple alterations occur in brain networks with healthy aging. Much of this has yet to be investigated. This study seeks to understand the typical healthy human brain's developmental stages using a publicly available dataset from the human connectome project. As the human brain's developmental stage varies, we also intend to identify a pattern of reorganization in the resting state functional connectivity of several brain networks. The results are specifically presented based on the resting state BOLD signals of 1096 healthy volunteers between the age group of 7-89 years. The k-means clustering method has been used to determine the various human brain developmental stages. Using the t-SNE technique, the clusters are visually separated. BrainNet Viewer is used to study the changes in resting state functional connectivity of the entire brain as the human brain developmental stages vary. The age-related pattern of change in the resting state functional connectivity of six Dosenbasch brain networks that were grouped using the k-means elbow approach has been additionally presented. For performance evaluation, three metrics of brain network connection including network segregation, between network connectivity, and within-network connectivity are used. As the age cohort changes, a consistent pattern in the variance of these connection indices is seen for different Dosenbasch brain networks. Thus, the study's findings suggest that healthy aging causes a functional reorganization of the resting state brain network connections.

Lifelong experiences as a proxy of cognitive reserve moderate the association between connectivity and cognition in Alzheimer's disease

Alzheimer's disease (AD) is associated with alterations in functional connectivity (FC) of the brain. The FC underpinnings of CR, that is, lifelong experiences, are largely unknown. Resting-state FC and structural MRI were performed in 76 CSF amyloid-β (Aβ) negative healthy controls and 152 Aβ positive individuals as an AD spectrum cohort (ADS; 55 with subjective cognitive decline, SCD; 52 with mild cognitive impairment; 45 with AD dementia). Following a region-of-interest (ROI) FC analysis, intrinsic network connectivity within the default-mode network (INC-DMN) and anti-correlation in INC between the DMN and dorsal attention network (DMN:DAN) were obtained as composite scores. CR was estimated by education and Lifetime Experiences Questionnaire (LEQ). The association between INC-DMN and MEM was attenuated by higher LEQ scores in the entire ADS group, particularly in SCD. In ROI analyses, higher LEQ scores were associated with higher FC within the DMN in ADS group. INC-DMN remains relatively intact despite memory decline in individuals with higher lifetime activity estimates, supporting a role for functional networks in maintaining cognitive function in AD.

Altered dynamics of glymphatic flow in a mature-onset Tet-off APP mouse model of amyloidosis

BACKGROUND

Alzheimer's disease (AD) is an incurable neurodegenerative disorder characterised by the progressive buildup of toxic amyloid-beta (Aβ) and tau protein aggregates eventually leading to cognitive decline. Recent lines of evidence suggest that an impairment of the glymphatic system (GS), a brain waste clearance pathway, plays a key role in the pathology of AD. Moreover, a relationship between GS function and neuronal network integrity has been strongly implicated. Here, we sought to assess the efficacy of the GS in a transgenic Tet-Off APP mouse model of amyloidosis, in which the expression of mutant APP was delayed until maturity, mimicking features of late-onset AD-the most common form of dementia in humans.

METHODS

To evaluate GS function, we used dynamic contrast-enhanced MRI (DCE-MRI) in 14-month-old Tet-Off APP (AD) mice and aged-matched littermate controls. Brain-wide transport of the Gd-DOTA contrast agent was monitored over time after cisterna magna injection. Region-of-interest analysis and computational modelling were used to assess GS dynamics while characterisation of brain tissue abnormalities at the microscale was performed ex vivo by immunohistochemistry.

RESULTS

We observed reduced rostral glymphatic flow and higher accumulation of the contrast agent in areas proximal to the injection side in the AD group. Clustering and subsequent computational modelling of voxel time courses revealed significantly lower influx time constants in AD relative to the controls. Ex vivo evaluation showed abundant amyloid plaque burden in the AD group coinciding with extensive astrogliosis and microgliosis. The neuroinflammatory responses were also found in plaque-devoid regions, potentially impacting brain-fluid circulation.

CONCLUSIONS

In a context resembling late-onset AD in humans, we demonstrate the disruption of glymphatic function and particularly a reduction in brain-fluid influx in the AD group. We conjecture that the hindered circulation of cerebrospinal fluid is potentially caused by wide-spread astrogliosis and amyloid-related obstruction of the normal routes of glymphatic flow resulting in redirection towards caudal regions. In sum, our study highlights the translational potential of alternative approaches, such as targeting brain-fluid circulation as potential therapeutic strategies for AD.

Resting-state fMRI signals contain spectral signatures of local hemodynamic response timing

Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, as differences can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.

Increased Functional Connectivity of the Precuneus in Individuals with a Family History of Alzheimer's Disease

BACKGROUND

First-degree relatives of individuals with late-onset Alzheimer's disease (AD) have increased risk for AD, with children of affected parents at an especially high risk.

OBJECTIVE

We aimed to investigate default mode network connectivity, medial temporal cortex volume, and cognition in cognitively healthy (CH) individuals with (FH+) and without (FH-) a family history of AD, alongside amnestic mild cognitive impairment (aMCI) and AD individuals, to determine the context and directionality of dysfunction in at-risk individuals. Our primary hypothesis was that there would be a linear decline (CH FH- > CH FH+ > aMCI > AD) within the risk groups on all measures of AD risk.

METHODS

We used MRI and fMRI to study cognitively healthy individuals (n = 28) with and without AD family history (FH+ and FH-, respectively), those with aMCI (n = 31) and early-stage AD (n = 25). We tested connectivity within the default mode network, as well as measures of volume and thickness within the medial temporal cortex and selected seed regions.

RESULTS

As expected, we identified decreased medial temporal cortex volumes in the aMCI and AD groups compared to cognitively healthy groups. We also observed patterns of connectivity across risk groups that suggest a nonlinear relationship of change, such that the FH+ group showed increased connectivity compared to the FH- and AD groups (CH FH+ > CH FH- > aMCI > AD). This pattern emerged primarily in connectivity between the precuneus and frontal regions.

CONCLUSION

These results add to a growing literature that suggests compensatory brain function in otherwise cognitively healthy individuals with a family history of AD.

Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network

Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

SDI-118, a novel procognitive SV2A modulator: First-in-human randomized controlled trial including PET/fMRI assessment of target engagement

Background: Current treatments for progressive neurodegenerative disorders characterized by cognitive impairment either have limited efficacy or are lacking altogether. SDI-118 is a small molecule which modulates the activity of synaptic vesicle glycoprotein 2A (SV2A) in the brain and shows cognitive enhancing effects in a range of animal models of cognitive deficit. Methods: This first-in-human study evaluated safety, tolerability, and pharmacokinetics/pharmacodynamics of SDI-118 in single ascending oral doses up to 80 mg administered to 32 healthy male subjects. Brain target occupancy was measured in eight subjects using positron emission tomography with PET-ligand [¹¹C]-UCB-J. Food effect was assessed in seven subjects. Mood state was regularly evaluated using standardized questionnaires, and resting state fMRI data were analyzed as exploratory objectives. Key Results: At all doses tested, SDI-118 was well tolerated and appeared safe. Adverse events were mainly dizziness, hypersomnia, and somnolence. All were mild in intensity and increased in frequency with increasing administered dose. No dose-limiting adverse reactions were observed at any dose. SDI-118 displayed a linear pharmacokinetic profile with no significant food effect. Brain penetration and target engagement were demonstrated by a dose-proportional SV2A occupancy. Conclusion: Single oral doses of SDI-118 up to 80 mg were very well tolerated in healthy male subjects. Dose-proportional SV2A occupancy in the brain was demonstrated with brain imaging. Adverse effects in humans mainly occurred in higher dose ranges, with high occupancy levels, and were all mild and self-limiting. These data support further clinical exploration of the compound in patients with cognitive disorders. Clinical Trial Registration: https://clinicaltrials.gov/, identifier NCT05486195.

Hippocampal functional connectivity across age in an App knock-in mouse model of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is a progressive neurodegenerative disease. The early processes of AD, however, are not fully understood and likely begin years before symptoms manifest. Importantly, disruption of the default mode network, including the hippocampus, has been implicated in AD.

METHODS

To examine the role of functional network connectivity changes in the early stages of AD, we performed resting-state functional magnetic resonance imaging (rs-fMRI) using a mouse model harboring three familial AD mutations (App NL-G-F/NL-G-F knock-in, APPKI) in female mice in early, middle, and late age groups. The interhemispheric and intrahemispheric functional connectivity (FC) of the hippocampus was modeled across age.

RESULTS

We observed higher interhemispheric functional connectivity (FC) in the hippocampus across age. This was reduced, however, in APPKI mice in later age. Further, we observed loss of hemispheric asymmetry in FC in APPKI mice.

DISCUSSION

Together, this suggests that there are early changes in hippocampal FC prior to heavy onset of amyloid β plaques, and which may be clinically relevant as an early biomarker of AD.

FMRI Complexity Correlates with Tau-PET and Cognitive Decline in Late-Onset and Autosomal Dominant Alzheimer's Disease

BACKGROUND

Neurofibrillary tangle pathology detected with tau-PET correlates closely with neuronal injury and cognitive symptoms in Alzheimer's disease (AD). Complexity of rs-fMRI has been demonstrated to decrease with cognitive decline in AD.

OBJECTIVE

We hypothesize that the rs-fMRI complexity provides an index for tau-related neuronal injury and cognitive decline in the AD process.

METHODS

Data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI3) and the Estudio de la Enfermedad de Alzheimer en Jalisciences (EEAJ) study. Associations between tau-PET and rs-fMRI complexity were calculated. Potential pathways relating complexity to cognitive function mediated through tau-PET were assessed by path analysis.

RESULTS

We found significant negative correlations between rs-fMRI complexity and tau-PET in medial temporal lobe of both cohorts, and associations of rs-fMRI complexity with cognitive scores were mediated through tau-PET.

CONCLUSION

The association of rs-fMRI complexity with tau-PET and cognition, suggests that a reduction in complexity is indicative of tau-related neuropathology and cognitive decline in AD processes.

Changes in Taste Perception in Patients with Minor and Major Cognitive Impairment Linked to Alzheimer's Disease Recorded by Gustatory Evoked Potentials

BACKGROUND

The need for early diagnosis biomarkers in Alzheimer's disease (AD) is growing. Only few studies have reported gustatory dysfunctions in AD using subjective taste tests.

OBJECTIVE

The main purpose of the study was to explore gustatory functions using subjective taste tests and recordings of gustatory evoked potentials (GEPs) for sucrose solution in patients with minor or major cognitive impairment (CI) linked to AD, and to compare them with healthy controls. The secondary objective was to evaluate the relationships between GEPs and the results of cognitive assessments and fasting blood samples.

METHODS

A total of 45 subjects (15 healthy subjects, 15 minor CI patients, 15 major CI patients) were included to compare their gustatory functions and brain activity by recording GEPs in response to a sucrose stimulation. CI groups were combined in second analyses in order to keep a high power in the study. Correlations were made with cognitive scores and hormone levels (ghrelin, leptin, insulin, serotonin).

RESULTS

Increased P1 latencies and reduced N1 amplitudes were observed in minor or major patients compared to controls. GEPs were undetectable in 6 major and 4 minor CI patients. Thresholds for sucrose detection were significantly higher in the major CI group than in controls or the minor CI group. No correlation was found with hormone levels.

CONCLUSIONS

The cortical processing of sensory taste information seems to be altered in patients with minor or major CI linked to AD. This disturbance was identifiable with subjective taste tests only later, at the major CI stage.

Medial Temporal Lobe Tau Aggregation Relates to Divergent Cognitive and Emotional Empathy Abilities in Alzheimer's Disease

BACKGROUND

In Alzheimer's disease (AD), the gradual accumulation of amyloid-β (Aβ) and tau proteins may underlie alterations in empathy.

OBJECTIVE

To assess whether tau aggregation in the medial temporal lobes related to differences in cognitive empathy (the ability to take others' perspectives) and emotional empathy (the ability to experience others' feelings) in AD.

METHODS

Older adults (n = 105) completed molecular Aβ positron emission tomography (PET) scans. Sixty-eight of the participants (35 women) were Aβ positive and symptomatic with diagnoses of mild cognitive impairment, dementia of the Alzheimer's type, logopenic variant primary progressive aphasia, or posterior cortical atrophy. The remaining 37 (22 women) were asymptomatic Aβ negative healthy older controls. Using the Interpersonal Reactivity Index, we compared current levels of informant-rated cognitive empathy (Perspective-Taking subscale) and emotional empathy (Empathic Concern subscale) in the Aβ positive and negative participants. The Aβ positive participants also underwent molecular tau-PET scans, which were used to investigate whether regional tau burden in the bilateral medial temporal lobes related to empathy.

RESULTS

Aβ positive participants had lower perspective-taking and higher empathic concern than Aβ negative healthy controls. Medial temporal tau aggregation in the Aβ positive participants had divergent associations with cognitive and emotional empathy. Whereas greater tau burden in the amygdala predicted lower perspective-taking, greater tau burden in the entorhinal cortex predicted greater empathic concern. Tau burden in the parahippocampal cortex did not predict either form of empathy.

CONCLUSIONS

Across AD clinical syndromes, medial temporal lobe tau aggregation is associated with lower perspective-taking yet higher empathic concern.

Brain Network Organization and Aging

Despite recent substantial progress in neuroscience, the mechanisms and principles of the complex structure, functions, and the relationship between the brain and cognitive functions have not been fully understood. The modeling method of brain network can provide a new perspective for neuroscience research, and it is possible to provide new solutions to the related research problems. On this basis, the researchers define the concept of human brain connectome to highlight and emphasize the importance of network modeling methods in neuroscience. For example, using diffusion-weighted magnetic resonance imaging (dMRI) technology and fiber tractography methods, a white matter connection network of the whole brain can be constructed. From the perspective of brain function, functional magnetic resonance imaging (fMRI) data can build the brain functional connection network. A structural covariation modeling method is used to obtain a brain structure covariation network, and it appears to reflect developmental coordination or synchronized maturation between areas of the brain. In addition, network modeling and analysis methods can also be applied to other types of image data, such as positron emission tomography (PET), electroencephalogram (EEG), and magnetoencephalography (MEG). This chapter mainly reviews the research progress of researchers on brain structure, function, and other aspects at the network level in recent years.

Deep brain stimulation for the treatment of Alzheimer's disease: A systematic review and meta-analysis

Background

One of the experimental neuromodulation techniques being researched for the treatment of Alzheimer's disease (AD) is deep brain stimulation (DBS). To evaluate the effectiveness of DBS in AD, we performed a systematic review and meta-analysis of the available evidence.

Methods

From the inception through December 2021, the following databases were searched: Medline via PubMed, Scopus, Embase, Cochrane Library, and Web of Science. The search phrases used were "Alzheimer's disease," "AD," "deep brain stimulation," and "DBS." The information from the included articles was gathered using a standardized data-collecting form. In the included papers, the Cochrane Collaboration methodology was used to evaluate the risk of bias. A fixed-effects model was used to conduct the meta-analysis.

Results

Only five distinct publications and 6 different comparisons (one study consisted of two phases) were included out of the initial 524 papers that were recruited. DBS had no impact on the cognitive ability in patients with AD [0.116 SMD, 95% confidence interval (CI), -0.236 to 0.469, p = 0.518]. The studies' overall heterogeneity was not significant (κ² = 6.23, T ² = 0.053, df = 5, I ² = 19.76%, p = 0.284). According to subgroup analysis, the fornix-DBS did not improve cognitive function in patients with AD (0.145 SMD, 95%CI, -0.246 to 0.537, p = 0.467). Unfavorable neurological and non-neurological outcomes were also reported.

Conclusion

The inconsistencies and heterogeneity of the included publications in various target and age groups of a small number of AD patients were brought to light by this meta-analysis. To determine if DBS is useful in the treatment of AD, further studies with larger sample sizes and randomized, double-blinded, sham-controlled designs are required.

Synaptic degeneration in Alzheimer disease

Alzheimer disease (AD) is characterized by progressive cognitive decline in older individuals accompanied by the presence of two pathological protein aggregates - amyloid-β and phosphorylated tau - in the brain. The disease results in brain atrophy caused by neuronal loss and synapse degeneration. Synaptic loss strongly correlates with cognitive decline in both humans and animal models of AD. Indeed, evidence suggests that soluble forms of amyloid-β and tau can cause synaptotoxicity and spread through neural circuits. These pathological changes are accompanied by an altered phenotype in the glial cells of the brain - one hypothesis is that glia excessively ingest synapses and modulate the trans-synaptic spread of pathology. To date, effective therapies for the treatment or prevention of AD are lacking, but understanding how synaptic degeneration occurs will be essential for the development of new interventions. Here, we highlight the mechanisms through which synapses degenerate in the AD brain, and discuss key questions that still need to be answered. We also cover the ways in which our understanding of the mechanisms of synaptic degeneration is leading to new therapeutic approaches for AD.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

Age-related differences in functional network segregation in the context of sex and reproductive stage

Age is accompanied by differences in the organization of functional brain networks, which impact behavior in adulthood. Functional networks become less segregated and more integrated with age. However, sex differences in network segregation declines with age are not well-understood. Further, network segregation in the context of female reproductive stage is relatively understudied, though unmasking such relationships would be informative for elucidating biological mechanisms that contribute to sex-specific differences in aging. In the current work, we used data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository to evaluate differences in resting-state network segregation as a product of sex and reproductive stage. Reproductive stage was categorized using the Stages of Reproductive Aging Workshop (STRAW+10) criteria. Replicating prior work, we investigated the following functional networks: auditory, cerebellar-basal ganglia, cingulo-opercular task control, default mode, dorsal attention, fronto-parietal task control, salience, sensory somatomotor mouth, sensory somatomotor hand, ventral attention, and visual. First, our results mirror findings from previous work indicating that network segregation is lower with increasing age. Second, when analyzing associations between network segregation and age within each sex separately, we find qualitative differences between females and males. Finally, we report significant effects of reproductive stage on network segregation, though these findings are likely driven by age. Broadly, our results suggest that impacts of sex may be important to evaluate when investigating network segregation differences across adulthood, though further work is needed to determine the unique role of menopause and sex hormones on the organization of functional brain networks within aging females.

Hippocampal Neuronal Integrity and Functional Connectivity Within the Default Mode Network in Mild Cognitive Impairment: A Multimodal Investigation

Background: In older people with mild cognitive impairment (MCI), the relationship between early changes in functional connectivity and in vivo changes in key neurometabolites is not known. Two established correlates of MCI diagnosis are decreased N-acetylaspartate (NAA) in the hippocampus, indicative of decreased neuronal integrity, and changes in the default mode network (DMN) functional network. If and how these measures interrelate is yet to be established, and such understanding may provide insight into the processes underpinning observed cognitive decline. Objectives: To determine the relationship between NAA levels in the left hippocampus and functional connectivity within the DMN in an aging cohort. Methods: In a sample of 51 participants with MCI and 30 controls, hippocampal NAA was determined using magnetic resonance spectroscopy, and DMN connectivity was quantified using resting-state functional MRI. The association between hippocampal NAA and the DMN functional connectivity was tested within the MCI group and separately within the control group. Results: In the DMN, we showed a significant inverse association between functional connectivity and hippocampal NAA in 20 specific brain connections for patients with MCI. This was despite no evidence of any associations in the healthy control group or group differences in either of these measures alone. Conclusions: This study suggests that decreased neuronal integrity in the hippocampus is associated with functional change within the DMN for those with MCI, in contrast to healthy older adults. These results highlight the potential of multimodal investigations to better understand the processes associated with cognitive decline. Impact statement This study measured activity within the default mode network (DMN) and quantified N-acetylaspartate (NAA), a measure of neuronal integrity, within the hippocampus in participants with mild cognitive impairment (MCI) and healthy controls. In participants with MCI, NAA levels were inversely associated with connectivity between specific regions of the DMN, a relationship not evident in healthy controls. This association was present even in the absence of group differences in DMN connectivity or NAA levels. This research illustrates the possibility of using multiple magnetic resonance modalities for more sensitive measures of early cognitive decline to identify and intervene earlier.

Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Disconnection Syndromes

In this review, the history and current status of the topic of disconnection syndromes, which was introduced to the discipline of Behavioral Neurology by the founding father Norman Geschwind and that has become the dominant paradigm for the explanation of neuropsychiatric disorders with new developments, like network connectivity imaging in the living human brain are discussed.

Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study

Introduction

Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression.

Methods

We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores.

Results

The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores.

Discussion

Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.

Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease

Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.

Single-index models with functional connectivity network predictors

Functional connectivity is defined as the undirected association between two or more functional magnetic resonance imaging (fMRI) time series. Increasingly, subject-level functional connectivity data have been used to predict and classify clinical outcomes and subject attributes. We propose a single-index model wherein response variables and sparse functional connectivity network valued predictors are linked by an unspecified smooth function in order to accommodate potentially nonlinear relationships. We exploit the network structure of functional connectivity by imposing meaningful sparsity constraints, which lead not only to the identification of association of interactions between regions with the response but also the assessment of whether or not the functional connectivity associated with a brain region is related to the response variable. We demonstrate the effectiveness of the proposed model in simulation studies and in an application to a resting-state fMRI data set from the Human Connectome Project to model fluid intelligence and sex and to identify predictive links between brain regions.

Deep brain stimulation of the nucleus basalis of Meynert modulates hippocampal-frontoparietal networks in patients with advanced Alzheimer's disease

BACKGROUND

Deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has shown potential for the treatment of mild-to-moderate Alzheimer's disease (AD). However, there is little evidence of whether NBM-DBS can improve cognitive functioning in patients with advanced AD. In addition, the mechanisms underlying the modulation of brain networks remain unclear. This study was aimed to assess the cognitive function and the resting-state connectivity following NBM-DBS in patients with advanced AD.

METHODS

Eight patients with advanced AD underwent bilateral NBM-DBS and were followed up for 12 months. Clinical outcomes were assessed by neuropsychological examinations using the Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale. Resting-state functional magnetic resonance imaging and positron emission tomography data were also collected.

RESULTS

The cognitive functioning of AD patients did not change from baseline to the 12-month follow-up. Interestingly, the MMSE score indicated clinical efficacy at 1 month of follow-up. At this time point, the connectivity between the hippocampal network and frontoparietal network tended to increase in the DBS-on state compared to the DBS-off state. Additionally, the increased functional connectivity between the parahippocampal gyrus (PHG) and the parietal cortex was associated with cognitive improvement. Further dynamic functional network analysis showed that NBM-DBS increased the proportion of the PHG-related connections, which was related to improved cognitive performance.

CONCLUSION

The results indicated that NBM-DBS improves short-term cognitive performance in patients with advanced AD, which may be related to the modulation of multi-network connectivity patterns, and the hippocampus plays an important role within these networks.

TRIAL REGISTRATION

ChiCTR, ChiCTR1900022324. Registered 5 April 2019-Prospective registration. https://www.chictr.org.cn/showproj.aspx?proj=37712.