Staging Alzheimer's Disease Risk by Sequencing Brain Function and Structure, Cerebrospinal Fluid, and Cognition Biomarkers

Distinct Functional Connectivity Patterns for Intermittent Vs Constant Neuropathic Pain Phenotypes in Persistent Spinal Pain Syndrome Type 2 Patients

Background

Chronic low back pain (cLBP) has been associated with alterations in brain functional connectivity (FC) but based upon heterogeneous populations and single network analyses. Our goal is to study a more homogeneous cLBP population and focus on multiple cross-network (CN) connectivity analysis. We hypothesize that within this population: 1) altered CN FC, involving emotion and reward/aversion functions are related to their pain levels and 2) altered relationships are dependent upon pain phenotype (constant neuropathic vs intermittent pain).

Methods

In this case series, resting state fcMRI scans were obtained over a study duration of 60 months from 23 patients (13 constant neuropathic and 10 intermittent pain) with Persistent Spinal Pain Syndrome (PSPS Type 2) being considered for spinal cord stimulation (SCS) therapy at a single academic center. Images were acquired using a Discovery MR750 GE scanner. During the resting state acquisitions, they were asked to close their eyes and relax. The CN analysis was performed on 7 brain networks and compared to age-matched controls. Linear regression was used to test the correlation between CN connectivity and pain scores.

Results

CN FC involving emotion networks (STM: striatum network index) was significantly lower than controls in all patients, regardless of pain phenotype (P < 0.003). Pain levels were positively correlated with emotional FC for intermittent pain but negatively correlated for constant pain.

Conclusion

This is the first report of 1) altered CN FC involving emotion/reward brain circuitry in 2) a homogeneous population of cLBP patients with 3) two different pain phenotypes (constant vs intermittent) in PSPS Type 2 patients being considered for SCS. FC patterns were altered in cLBP patients as compared to controls and were characteristic for each pain phenotype. These data support fcMRI as a potential and objective tool in assessing pain levels in cLBP patients with different pain phenotypes.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Functional Magnetic Resonance Imaging in Alzheimer's Disease Drug Trials: A Mini-Review

Background

Alzheimer's disease (AD) is a progressive neurodegenerative pathology that leads to cognitive decline and dementia, particularly in older adults. It disrupts brain structure and function, with neurotoxic amyloid-β (Aβ) plaques being a primary pathological hallmark. Pharmacotherapeutic trials targeting Aβ and other AD pathological features aim to slow disease progression. Functional magnetic resonance imaging (fMRI) is a non-invasive tool that visualizes brain functional activity, aiding in evaluating the efficacy of AD drugs in clinical trials.

Objective

This mini-review explores the role of fMRI in evaluating the impact of AD pharmacotherapeutic clinical trials conducted in the past seven years.

Methods

Literature was systematically searched using two databases. The risk of bias was assessed with the Revised Cochrane risk-of-bias tool (RoB-2) for randomized clinical trials (RCTs).

Results

Four studies using fMRI to investigate AD drug efficacy were included. Cholinesterase, glutamatergic, and serotonergic drugs showed significant positive effects on brain functional activity, especially within the default mode network. Functional connectivity (FC) changes due to drug intake were linked to cerebellar and cholinergic decline in AD, correlating with improved global cognition and fMRI task performance.

Conclusions

Recent RCTs demonstrate fMRI's ability to reveal longitudinal FC pattern changes in response to AD drug treatments across disease stages. Positive FC changes in distinct brain regions suggest potential compensatory mechanisms from drug intake. However, these drugs have limited efficacy, necessitating further research to enhance specific pharmacological interventions for clinical application.

Functional Connectivity Magnetic Resonance Imaging Sequences in Patients With Postsurgical Persistent Spinal Pain Syndrome Type 2 With Implanted Spinal Cord Stimulation Systems: A Safety, Feasibility, and Validity Study

BACKGROUND

Chronic pain has been associated with alterations in brain connectivity, both within networks (regional) and between networks (cross-network connectivity). Functional connectivity (FC) data on chronic back pain are limited and based on heterogeneous pain populations. Patients with postsurgical persistent spinal pain syndrome (PSPS) type 2 are good candidates for spinal cord stimulation (SCS) therapy. We hypothesize that 1) FC magnetic resonance imaging (fcMRI) scans can be safely obtained in patients with PSPS type 2 with implanted therapeutic SCS devices and that 2) their cross-network connectivity patterns are altered and involve emotion and reward/aversion functions.

MATERIALS AND METHODS

Resting-state (RS) fcMRI (rsfcMRI) scans were obtained from nine patients with PSPS type 2 implanted with therapeutic SCS systems and 13 age-matched controls. Seven RS networks were analyzed, including the striatum.

RESULTS

Cross-network FC sequences were safely obtained on a 3T MRI scanner in all nine patients with PSPS type 2 with implanted SCS systems. FC patterns involving emotion/reward brain circuitry were altered as compared with controls. Patients with a history of constant neuropathic pain, experiencing longer therapeutic effects of SCS, had fewer alterations in their connectivity patterns.

CONCLUSIONS

To our knowledge, this is the first report of altered cross-network FC involving emotion/reward brain circuitry in a homogeneous population of patients with chronic pain with fully implanted SCS systems, on a 3T MRI scanner. All rsfcMRI studies were safe and well tolerated by all nine patients, with no detectable effects on the implanted devices.

PICALM rs3851179 Variants Modulate Left Postcentral Cortex Thickness, CSF Amyloid β42, and Phosphorylated Tau in the Elderly

PICALM rs3851179, one of the genes most frequently linked to susceptibility of late-onset Alzheimer's disease (LOAD), plays a crucial role in regulating amyloid precursor protein, and amyloid β (Aβ) transcytosis. To explore the effects of PICALM and AD continuum stage on cortex thickness, CSF Aβ, and tau, 188 cognitively normal controls, 261 MCI patients, and 140 early LOAD patients were recruited, and each group was divided into rs3851179 A-carriers and GG-carriers. A full factorial ANCOVA was used to analyze the main effects and interactive effects of AD continuum stage, and PICALM. The interactive effects of AD continuum stage and PICALM on cortex thickness and CSF biomarkers were not significant. The main effect of PICALM was significant on the left postcentral cortex thickness, and the cortex thickness of A-carriers was less than that of GG-carriers. The rs3851179 A-carriers displayed higher Aβ42 levels and Aβ42/40 ratios, and lower P/T-tau ratios, compared with GG-carriers. A higher MMSE score was found in A-carriers among the LOAD patients. In conclusion, the main effects of PICALM were independent of AD continuum stage, and PICLAM rs3851179 genotypes may modulate left postcentral cortex thickness, Aβ42 level, and P/T-tau ratio. The rs3851179 A-allele may protect the cognitive function of LOAD patients.

A Computational Monte Carlo Simulation Strategy to Determine the Temporal Ordering of Abnormal Age Onset Among Biomarkers of Alzheimer's Disease

To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.

Imminent cognitive decline in normal elderly individuals is associated with hippocampal hyperconnectivity in the variant neural correlates of episodic memory

The secondary prevention trials of Alzheimer's disease (AD) require an enrichment strategy to recruit individuals with imminent cognitive decline at the preclinical stage. Previously, we demonstrated a variant neural correlates of episodic memory (EM) function in apolipoprotein E (APOE) ε4 carriers. Herein, we investigated whether this variation was associated with longitudinal EM performance. This 3-year longitudinal study included 88 normal elderly subjects with EM assessment and resting-state functional MRI data at baseline; 48 subjects (27 ε3 homozygotes and 21 ε4 carriers) underwent follow-up EM assessment. In the identified EM neural correlates, multivariable regression models examined the association between hippocampal functional connectivity (HFC) and longitudinal EM change. Independent validation was performed using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. At baseline, the EM neural correlates were characterized in the Papez circuit regions in the ε3 homozygotes, but in the sensorimotor cortex and cuneus in the ε4 carriers. Longitudinally, the ε4 carriers exhibited a negative association of the baseline HFC strength in the EM neural correlates with annual rate of EM change (R² = 0.25, p = 0.05). This association also showed a trend in the ADNI dataset (R² = 0.42, p = 0.06). These results indicate that hippocampal hyperconnectivity in the variant EM neural correlates is associated with imminent EM decline in ε4 carriers, which may serve as a promising enrichment strategy for secondary prevention trials of AD.

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease

BACKGROUND

Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged.

METHODS

We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort.

RESULTS

We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory.

CONCLUSION

Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.

A Neuropsychological Perspective on Defining Cognitive Impairment in the Clinical Study of Alzheimer’s Disease: Towards a More Continuous Approach

The global fight against Alzheimer’s disease (AD) poses unique challenges for the field of neuropsychology. Along with the increased focus on early detection of AD pathophysiology, characterizing the earliest clinical stage of the disease has become a priority. We believe this is an important time for neuropsychology to consider how our approach to the characterization of cognitive impairment can be improved to detect subtle cognitive changes during early-stage AD. The present article aims to provide a critical examination of how we define and measure cognitive status in the context of aging and AD. First, we discuss pitfalls of current methods for defining cognitive impairment within the context of research shifting to earlier (pre)symptomatic disease stages. Next, we introduce a shift towards a more continuous approach for identifying early markers of cognitive decline and characterizing progression and discuss how this may be facilitated by novel assessment approaches. Finally, we summarize potential implications and challenges of characterizing cognitive status using a continuous approach.

Multiple Connection Pattern Combination From Single-Mode Data for Mild Cognitive Impairment Identification

Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer’s disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern.

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort

A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.

tDCS-Induced Memory Reconsolidation Effects and Its Associations With Structural and Functional MRI Substrates in Subjective Cognitive Decline

Previous evidence suggests that transcranial direct current stimulation (tDCS) to the left dorsolateral prefrontal cortex (l-DLPFC) can enhance episodic memory in subjects with subjective cognitive decline (SCD), known to be at risk of dementia. Our main goal was to replicate such findings in an independent sample and elucidate if baseline magnetic resonance imaging (MRI) characteristics predicted putative memory improvement. Thirty-eight participants with SCD (aged: 60-65 years) were randomly assigned to receive active (N = 19) or sham (N = 19) tDCS in a double-blind design. They underwent a verbal learning task with 15 words (DAY-1), and 24 h later (DAY-2) stimulation was applied for 15 min at 1.5 mA targeting the l-DLPFC after offering a contextual reminder. Delayed recall and recognition were measured 1 day after the stimulation session (DAY-3), and at 1-month follow-up (DAY-30). Before the experimental session, structural and functional MRI were acquired. We identified a group∗time interaction in recognition memory, being the active tDCS group able to maintain stable memory performance between DAY-3 and DAY-30. MRI results revealed that individuals with superior tDCS-induced effects on memory reconsolidation exhibited higher left temporal lobe thickness and greater intrinsic FC within the default-mode network. Present findings confirm that tDCS, through the modulation of memory reconsolidation, is capable of enhancing performance in people with self-perceived cognitive complaints. Results suggest that SCD subjects with more preserved structural and functional integrity might benefit from these interventions, promoting maintenance of cognitive function in a population at risk to develop dementia.

A social dancing pilot intervention for older adults at high risk for Alzheimer's disease and related dementias

Close to 6 million older US adults have Alzheimer's disease or related dementias, yet there is currently no cure or effective treatment. This single-blind randomized controlled trial (clinicaltrials.gov: NCT03475316) aims to establish feasibility, and explore the relative efficacy, of a 6-month social ballroom dancing intervention versus a 6-month active control intervention (treadmill walking) for improving executive function in 32 older adults at increased risk for Alzheimer's disease or related dementias. Dementia-at-risk status is determined with cut-scores on the memory impairment screen (≥3 to ≤6) and/or the AD8 Dementia Screening Interview (≥1). The primary outcome is a composite executive function score from digit-symbol substitution, flanker interference and walking-while-talking tasks. The secondary outcome is functional neuroplasticity during fMRI-adapted versions of digit-symbol substitution, flanker interference and walking-while-talking.

Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model

AIMS

Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population.

METHODS

The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.

RESULTS

AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2  = .17, P < .001) at baseline in the combined high-risk population (N = 72).

CONCLUSIONS

The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.

The Role of Resting-State Network Functional Connectivity in Cognitive Aging

Aging is associated with disruptions in the resting-state functional architecture of the brain. Previous studies have primarily focused on age-related declines in the default mode network (DMN) and its implications in Alzheimer's disease. However, due to mixed findings, it is unclear if changes in resting-state network functional connectivity are linked to cognitive decline in healthy older adults. In the present study, we evaluated the influence of intra-network coherence for four higher-order cognitive resting-state networks on a sensitive measure of cognitive aging (i.e., NIH Toolbox Fluid Cognition Battery) in 154 healthy older adults with a mean age of 71 and education ranging between 12 years and 21 years (mean = 16). Only coherence within the cingulo-opercular network (CON) was significantly related to Fluid Cognition Composite scores, explaining more variance in scores than age and education. Furthermore, we mapped CON connectivity onto fluid cognitive subdomains that typically decline in advanced age. Greater CON connectivity was associated with better performance on episodic memory, attention, and executive function tasks. Overall, the present study provides evidence to propose CON coherence as a potential novel neural marker for nonpathological cognitive aging.

Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event-based model of disease progression

Introduction

This work aims to characterize the sequence in which cognitive deficits appear in two dementia syndromes.

Methods

Event‐based modeling estimated fine‐grained sequences of cognitive decline in clinically‐diagnosed posterior cortical atrophy (PCA) (n=94) and typical Alzheimer's disease (tAD) (n=61) at the UCL Dementia Research Centre. Our neuropsychological battery assessed memory, vision, arithmetic, and general cognition. We adapted the event‐based model to handle highly non‐Gaussian data such as cognitive test scores where ceiling/floor effects are common.

Results

Experiments revealed differences and similarities in the fine‐grained ordering of cognitive decline in PCA (vision first) and tAD (memory first). Simulation experiments reveal that our new model equals or exceeds performance of the classic event‐based model, especially for highly non‐Gaussian data.

Discussion

Our model recovered realistic, phenotypical progression signatures that may be applied in dementia clinical trials for enrichment, and as a data‐driven composite cognitive end‐point.

Predicting progression from mild cognitive impairment to Alzheimer's disease on an individual subject basis by applying the CARE index across different independent cohorts

The purposes of this study are to investigate whether the Characterizing Alzheimer's disease Risk Events (CARE) index can accurately predict progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) on an individual subject basis, and to investigate whether this model can be generalized to an independent cohort. Using an event-based probabilistic model approach to integrate widely available biomarkers from behavioral data and brain structural and functional imaging, we calculated the CARE index. We then applied the CARE index to identify which MCI individuals from the ADNI dataset progressed to AD during a three-year follow-up period. Subsequently, the CARE index was generalized to the prediction of MCI individuals from an independent Nanjing Aging and Dementia Study (NADS) dataset during the same time period. The CARE index achieved high prediction performance with 80.4% accuracy, 75% sensitivity, 82% specificity, and 0.809 area under the receiver operating characteristic (ROC) curve (AUC) on MCI subjects from the ADNI dataset over three years, and a highly validated prediction performance with 87.5% accuracy, 81% sensitivity, 90% specificity, and 0.861 AUC on MCI subjects from the NADS dataset. In conclusion, the CARE index is highly accurate, sufficiently robust, and generalized for predicting which MCI individuals will develop AD over a three-year period. This suggests that the CARE index can be usefully applied to select individuals with MCI for clinical trials and to identify which individuals will convert from MCI to AD for administration of early disease-modifying treatment.

Reduced Dynamic Complexity of BOLD Signals Differentiates Mild Cognitive Impairment From Normal Aging

Mild cognitive impairment (MCI) is characterized as a transitional phase between cognitive decline associated with normal aging and Alzheimer’s disease (AD). Resting-state functional magnetic resonance imaging (fMRI) measuring blood oxygenation level-dependent (BOLD) signals provides complementary information considered essential for understanding disease progression. Previous studies suggested that multi-scale entropy (MSE) analysis quantifying the complexity of BOLD signals is a novel and promising method for investigating neurodegeneration associated with cognitive decline in different stages of MCI. Therefore, the current study used MSE to explore the changes in the complexity of resting-state brain BOLD signals in patients with early MCI (EMCI) and late MCI (LMCI). We recruited 345 participants’ data from the Alzheimer’s Disease Neuroimaging Initiative database, including 176 normal control (NC) subjects, 87 patients with EMCI and 82 patients with LMCI. We observed a significant reduction of brain signal complexity toward regularity in the left fusiform gyrus region in the EMCI group and in the rostral anterior cingulate cortex in the LMCI group. Our results extend prior work by revealing that significant reductions of brain BOLD signal complexity can be detected in different stages of MCI independent of age, sex and regional atrophy. Notably, the reduction of BOLD signal complexity in the rostral anterior cingulate cortex was significantly associated with greater risk of progression to AD. The present study thus identified MSE as a potential imaging biomarker for the early diagnosis of pre-clinical Alzheimer’s disease and provides further insights into the neuropathology of cognitive decline in prodromal AD.

Contributions by the Brain Renin-Angiotensin System to Memory, Cognition, and Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive neuron losses in memory-associated brain structures that rob patients of their dignity and quality of life. Five drugs have been approved by the FDA to treat AD but none modify or significantly slow disease progression. New therapies are needed to delay the course of this disease with the ultimate goal of preventing neuron losses and preserving memory functioning. In this review we describe the renin-angiotensin II (AngII) system (RAS) with specific regard to its deleterious contributions to hypertension, facilitation of neuroinflammation and oxidative stress, reduced cerebral blood flow, tissue remodeling, and disruption of memory consolidation and retrieval. There is evidence that components of the RAS, AngIV and Ang(1-7), are positioned to counter such damaging influences and these systems are detailed with the goal of drawing attention to their importance as drug development targets. Ang(1-7) binds at the Mas receptor, while AngIV binds at the AT4 receptor subtype, and these receptor numbers are significantly decreased in AD patients, accompanied by declines in brain aminopeptidases A and N, enzymes essential for the synthesis of AngIV. Potent analogs may be useful to counter these changes and facilitate neuronal functioning and reduce apoptosis in memory associated brain structures of AD patients.

Functional connectivity and structural analysis of trial spinal cord stimulation responders in failed back surgery syndrome

Background

Chronic pain has been associated with alterations in brain structure and function that appear dependent on pain phenotype. Functional connectivity (FC) data on chronic back pain (CBP) is limited and based on heterogeneous pain populations. We hypothesize that failed back surgery syndrome (FBSS) patients being considered for spinal cord stimulation (SCS) therapy have altered resting state (RS) FC cross-network patterns that 1) specifically involve emotion and reward/aversion functions and 2) are related to pain scores.

Methods

RS functional MRI (fMRI) scans were obtained for 10 FBSS patients who are being considered for but who have not yet undergone implantation of a permanent SCS device and 12 healthy age-matched controls. Seven RS networks were analyzed including the striatum (STM). The Wilcoxon signed-rank test evaluated differences in cross-network FC strength (FCS). Differences in periaqueductal grey (PAG) FC were assessed with seed-based analysis.

Results

Cross-network FCS was decreased (p<0.05) between the STM and all other networks in these FBSS patients. There was a negative linear relationship (R² = 0.76, p<0.0022) between STM_FCS index and pain scores. The PAG showed decreased FC with network elements and amygdala but increased FC with the sensorimotor cortex and cingulate gyrus.

Conclusions

Decreased FC between STM and other RS networks in FBSS has not been previously reported. This STM_FCS index may represent a more objective measure of chronic pain specific to FBSS which may help guide patient selection for SCS and subsequent management.

Differential tDCS and tACS Effects on Working Memory-Related Neural Activity and Resting-State Connectivity

Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) entail capability to modulate human brain dynamics and cognition. However, the comparability of these approaches at the level of large-scale functional networks has not been thoroughly investigated. In this study, 44 subjects were randomly assigned to receive sham (N = 15), tDCS (N = 15), or tACS (N = 14). The first electrode (anode in tDCS) was positioned over the left dorsolateral prefrontal cortex, the target area, and the second electrode (cathode in tDCS) was placed over the right supraorbital region. tDCS was delivered with a constant current of 2 mA. tACS was fixed to 2 mA peak-to-peak with 6 Hz frequency. Stimulation was applied concurrently with functional magnetic resonance imaging (fMRI) acquisitions, both at rest and during the performance of a verbal working memory (WM) task. After stimulation, subjects repeated the fMRI WM task. Our results indicated that at rest, tDCS increased functional connectivity particularly within the default-mode network (DMN), while tACS decreased it. When comparing both fMRI WM tasks, it was observed that tDCS displayed decreased brain activity post-stimulation as compared to online. Conversely, tACS effects were driven by neural increases online as compared to post-stimulation. Interestingly, both effects primarily occurred within DMN-related areas. Regarding the differences in each fMRI WM task, during the online fMRI WM task, tACS engaged distributed neural resources which did not overlap with the WM-dependent activity pattern, but with some posterior DMN regions. In contrast, during the post-stimulation fMRI WM task, tDCS strengthened prefrontal DMN deactivations, being these activity reductions associated with faster responses. Furthermore, it was observed that tDCS neural responses presented certain consistency across distinct fMRI modalities, while tACS did not. In sum, tDCS and tACS modulate fMRI-derived network dynamics differently. However, both effects seem to focus on DMN regions and the WM network-DMN shift, which are highly affected in aging and disease. Thus, albeit exploratory and needing further replication with larger samples, our results might provide a refined understanding of how the DMN functioning can be externally modulated through commonly used non-invasive brain stimulation techniques, which may be of eventual clinical relevance.

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

Mitochondria and Mitochondrial Cascades in Alzheimer's Disease

Decades of research indicate mitochondria from Alzheimer's disease (AD) patients differ from those of non-AD individuals. Initial studies revealed structural differences, and subsequent studies showed functional deficits. Observations of structure and function changes prompted investigators to consider the consequences, significance, and causes of AD-related mitochondrial dysfunction. Currently, extensive research argues mitochondria may mediate, drive, or contribute to a variety of AD pathologies. The perceived significance of these mitochondrial changes continues to grow, and many currently believe AD mitochondrial dysfunction represents a reasonable therapeutic target. Debate continues over the origin of AD mitochondrial changes. Some argue amyloid-β (Aβ) induces AD mitochondrial dysfunction, a view that does not challenge the amyloid cascade hypothesis and that may in fact help explain that hypothesis. Alternatively, data indicate mitochondrial dysfunction exists independent of Aβ, potentially lies upstream of Aβ deposition, and suggest a primary mitochondrial cascade hypothesis that assumes mitochondrial pathology hierarchically supersedes Aβ pathology. Mitochondria, therefore, appear at least to mediate or possibly even initiate pathologic molecular cascades in AD. This review considers studies and data that inform this area of AD research.

Age-related differences in default-mode network connectivity in response to intermittent theta-burst stimulation and its relationships with maintained cognition and brain integrity in healthy aging

The default-mode network (DMN) is affected by advancing age, where particularly long-range connectivity has been consistently reported to be reduced as compared to young individuals. We examined whether there were any differences in the effects of intermittent theta-burst stimulation (iTBS) in DMN connectivity between younger and older adults, its associations with cognition and brain integrity, as well as with long-term cognitive status. Twenty-four younger and 27 cognitively normal older adults were randomly assigned to receive real or sham iTBS over the left inferior parietal lobule between two resting-state functional magnetic resonance imaging (rs-fMRI) acquisitions. Three years later, those older adults who had received real iTBS underwent a cognitive follow-up assessment. Among the younger adults, functional connectivity increased following iTBS in distal DMN areas from the stimulation site. In contrast, older adults exhibited increases in connectivity following iTBS in proximal DMN regions. Moreover, older adults with functional responses to iTBS resembling those of the younger participants exhibited greater brain integrity and higher cognitive performance at baseline and at the 3-year follow-up, along with less cognitive decline. Finally, we observed that 'young-like' functional responses to iTBS were also related to the educational background attained amongst older adults. The present study reveals that functional responses of the DMN to iTBS are modulated by age. Furthermore, combining iTBS and rs-fMRI in older adults may allow characterizing distinctive cognitive profiles in aging and its progression, probably reflecting network plasticity systems that may entail a neurobiological substrate of cognitive reserve.

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

INTRODUCTION

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials.

METHODS

We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/).

RESULTS

(1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies.

DISCUSSION

ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain's connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain's anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer's Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer's disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer's disease. Our experimental results reveal new insights into Alzheimer's disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases.

β-Amyloid and the Pathomechanisms of Alzheimer's Disease: A Comprehensive View

Protein dyshomeostasis is the common mechanism of neurodegenerative diseases such as Alzheimer’s disease (AD). Aging is the key risk factor, as the capacity of the proteostasis network declines during aging. Different cellular stress conditions result in the up-regulation of the neurotrophic, neuroprotective amyloid precursor protein (APP). Enzymatic processing of APP may result in formation of toxic Aβ aggregates (β-amyloids). Protein folding is the basis of life and death. Intracellular Aβ affects the function of subcellular organelles by disturbing the endoplasmic reticulum-mitochondria cross-talk and causing severe Ca²⁺-dysregulation and lipid dyshomeostasis. The extensive and complex network of proteostasis declines during aging and is not able to maintain the balance between production and disposal of proteins. The effectivity of cellular pathways that safeguard cells against proteotoxic stress (molecular chaperones, aggresomes, the ubiquitin-proteasome system, autophagy) declines with age. Chronic cerebral hypoperfusion causes dysfunction of the blood-brain barrier (BBB), and thus the Aβ-clearance from brain-to-blood decreases. Microglia-mediated clearance of Aβ also declines, Aβ accumulates in the brain and causes neuroinflammation. Recognition of the above mentioned complex pathogenesis pathway resulted in novel drug targets in AD research.