Regional functional synchronizations in dementia with Lewy bodies and Alzheimer's disease

Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia

PURPOSE OF THE REVIEW

In this review, we attempt to summarize the most updated studies that applied resting-state functional magnetic resonance imaging (rs-fMRI) in the field of Parkinsonisms and related dementia.

RECENT FINDINGS

Over the past decades, increasing interest has emerged on investigating the presence and pathophysiology of cognitive symptoms in Parkinsonisms and their possible role as predictive biomarkers of neurodegenerative brain processes. In recent years, evidence has been provided, applying mainly three methodological approaches (i.e. seed-based, network-based and graph-analysis) on rs-fMRI data, with promising results. Neural correlates of cognitive impairment and dementia have been detected in patients with Parkinsonisms along the diseases course. Interestingly, early functional connectivity signatures were proposed to track and predict future progression of neurodegenerative processes. However, longitudinal studies are still sparce and further investigations are needed to overcome this knowledge gap.

Dementia Subtypes Defined Through Neuropsychiatric Symptom-Associated Brain Connectivity Patterns

Importance

Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia.

Objective

To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes.

Design, Setting, and Participants

Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited.

Main Outcomes and Measures

A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined.

Results

Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time × subtype 3 interaction item as the reference level: β = 0.05; t = 2.6 for time × subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI.

Conclusions and Relevance

These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.

A systematic literature review of fMRI and EEG resting-state functional connectivity in Dementia with Lewy Bodies: Underlying mechanisms, clinical manifestation, and methodological considerations

Previous studies suggest that there may be important links between functional connectivity, disease mechanisms underpinning the Dementia with Lewy Body (DLB) and the key clinical symptoms, but the exact relationship remains unclear. We performed a systematic literature review to address this gap by summarising the research findings while critically considering the impact of methodological differences on findings. The main methodological choices of fMRI articles included data-driven, seed-based or regions of interest approaches, or their combinations. Most studies focused on examining large-scale resting-state networks, which revealed a consistent decrease in connectivity and some associations with non-cognitive symptoms. Although the inter-network connectivity showed mixed results, the main finding is consistent with theories positing disconnection between visual and attentional areas of the brain implicated in the aetiology of psychotic symptoms in the DLB. The primary methodological choice of EEG studies was implementing the phase lag index and using graph theory. The EEG studies revealed a consistent decrease in connectivity on alpha and beta frequency bands. While the overall trend of findings showed decreased connectivity, more subtle changes in the directionality of connectivity were observed when using a hypothesis-driven approach. Problems with cognition were also linked with greater functional connectivity disturbances. In summary, connectivity measures can capture brain disturbances in the DLB and remain crucial in uncovering the causal relationship between the networks' disorganisation and underlying mechanisms resulting in psychotic, motor, and cognitive symptoms of the DLB.

Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns

BACKGROUND

Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia.

METHODS

Using large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes.

RESULTS

We identified two reliable neuropsychiatric subsyndromes - behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction.

CONCLUSIONS

Our findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients.

Reorganization of rich clubs in functional brain networks of dementia with Lewy bodies and Alzheimer's disease

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DLB and AD had the different functional reorganization patterns.

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Rich club nodes increased in frontal-parietal network in patients with DLB.

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The rich club nodes in temporal lobe decreased and those in cerebellum increased for AD.

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Compared with HC, rich club connectivity was enhanced in the DLB and AD groups.

The purpose of this study was to reveal the patterns of reorganization of rich club organization in brain functional networks in dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD). The study found that the rich club node shifts from sensory/somatomotor network to fronto-parietal network in DLB. For AD, the rich club nodes switch between the temporal lobe with obvious structural atrophy and the frontal lobe, parietal lobe and cerebellum with relatively preserved structure and function. In addition, compared with healthy controls, rich club connectivity was enhanced in the DLB and AD groups. The connection strength of DLB patients was related to cognitive assessment. In conclusion, we revealed the different functional reorganization patterns of DLB and AD. The conversion and redistribution of rich club members may play a causal role in disease-specific outcomes. It may be used as a potential biomarker to provide more accurate prevention and treatment strategies.

Neuroimaging alterations in dementia with Lewy bodies and neuroimaging differences between dementia with Lewy bodies and Alzheimer's disease: An activation likelihood estimation meta-analysis

Aims

The aim of this study was to identify brain regions with local, structural, and functional abnormalities in dementia with Lewy bodies (DLB) and uncover the differences between DLB and Alzheimer's disease (AD). The neural networks involved in the identified abnormal brain regions were further described.

Methods

PubMed, Web of Science, OVID, Science Direct, and Cochrane Library databases were used to identify neuroimaging studies that included DLB versus healthy controls (HCs) or DLB versus AD. The coordinate‐based meta‐analysis and functional meta‐analytic connectivity modeling were performed using the activation likelihood estimation algorithm.

Results

Eleven structural studies and fourteen functional studies were included in this quantitative meta‐analysis. DLB patients showed a dysfunction in the bilateral inferior parietal lobule and right lingual gyrus compared with HC patients. DLB patients showed a relative preservation of the medial temporal lobe and a tendency of lower metabolism in the right lingual gyrus compared with AD. The frontal‐parietal, salience, and visual networks were all abnormally co‐activated in DLB, but the default mode network remained normally co‐activated compared with AD.

Conclusions

The convergence of local brain regions and co‐activation neural networks might be potential specific imaging markers in the diagnosis of DLB. This might provide a pathway for the neural regulation in DLB patients, and it might contribute to the development of specific interventions for DLB and AD.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.

Lewy Body Dementias: A Coin with Two Sides?

Lewy body dementias (LBDs) consist of dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), which are clinically similar syndromes that share neuropathological findings with widespread cortical Lewy body deposition, often with a variable degree of concomitant Alzheimer pathology. The objective of this article is to provide an overview of the neuropathological and clinical features, current diagnostic criteria, biomarkers, and management of LBD. Literature research was performed using the PubMed database, and the most pertinent articles were read and are discussed in this paper. The diagnostic criteria for DLB have recently been updated, with the addition of indicative and supportive biomarker information. The time interval of dementia onset relative to parkinsonism remains the major distinction between DLB and PDD, underpinning controversy about whether they are the same illness in a different spectrum of the disease or two separate neurodegenerative disorders. The treatment for LBD is only symptomatic, but the expected progression and prognosis differ between the two entities. Diagnosis in prodromal stages should be of the utmost importance, because implementing early treatment might change the course of the illness if disease-modifying therapies are developed in the future. Thus, the identification of novel biomarkers constitutes an area of active research, with a special focus on α-synuclein markers.

Intrinsic connectivity identifies the sensory-motor network as a main cross-network between remitted late-life depression- and amnestic mild cognitive impairment-targeted networks

Remitted late-life depression (rLLD) and amnestic mild cognitive impairment (aMCI) are both associated with a high risk of developing Alzheimer's disease (AD). Neurodegeneration is considered to spread within pre-existing networks. To investigate whether, in the healthy brain, there was a pre-existing cross-network between the intrinsic networks that are vulnerable to rLLD and aMCI. We performed functional connectivity analyses based on brain areas with the greatest brain neuronal activity differences in 55 rLLD, 87 aMCI, and 114 healthy controls. Intrinsic networks that were differentially vulnerable to rLLD and aMCI converged onto the sensory-motor network (SMN) in the healthy brain. These regions in the SMN within the aMCI- and rLLD-vulnerable networks played different roles in the cognitive functions. This study identifies the SMN as a cross-network between rLLD- and aMCI-vulnerable networks. The common susceptibility of these diseases to AD is likely due to the breakdown of the cross-network. The results further suggest that interventions targeting the amelioration of sensory-motor deficits in the early course of disease in individuals with AD risk may enhance patient function as AD pathology progresses.

Metabolic connectivity-based single subject classification by multi-regional linear approximation in the rat

Metabolic connectivity patterns on the basis of [¹⁸F]-FDG positron emission tomography (PET) are used to depict complex cerebral network alterations in different neurological disorders and therefore may have the potential to support diagnostic decisions. In this study, we established a novel statistical classification method taking advantage of differential time-dependent states of whole-brain metabolic connectivity following unilateral labyrinthectomy (UL) in the rat and explored its classification accuracy. The dataset consisted of repeated [¹⁸F]-FDG PET measurements at baseline and 1, 3, 7, and 15 days (= maximum of 5 classes) after UL with 17 rats per measurement day. Classification in different stages after UL was performed by determining connectivity patterns for the different classes by Pearson's correlation between uptake values in atlas-based segmented brain regions. Connections were fitted with a linear function, with which different thresholds on the correlation coefficient (r = [0.5, 0.85]) were investigated. Rats were classified by determining the congruence of their PET uptake pattern with the fitted connectivity patterns in the classes. Overall, the classification accuracy with this method was 84.3% for 3 classes, 75.0% for 4 classes, and 54.1% for 5 classes and outperformed random classification as well as machine learning classification on the same dataset. The optimal classification thresholds of the correlation coefficient and distance-to-fit were found to be |r| > 0.65 and d = 4 when using Siegel's slope estimator for fitting. This connectivity-based classification method can compete with machine learning classification and may have methodological advantages when applied to support PET-based diagnostic decisions in neurological network disorders (such as neurodegenerative syndromes).

Frequency-dependent changes in fractional amplitude of low-frequency oscillations in Alzheimer's disease: a resting-state fMRI study

Alzheimer's disease (AD) is the most common neurodegenerative disease in elderly individuals. We conducted this study to examine whether alterations in the fractional amplitudes of low-frequency fluctuations (fALFF) in the AD spectrum were frequency-dependent and symptom-relevant. A total of 43 patients with subjective cognitive decline (SCD), 52 with amnestic mild cognitive impairment (aMCI), 44 with Alzheimer's dementia (d-AD) and 55 well-matched controls participated in resting-state functional magnetic resonance imaging (rs-fMRI) scans. The amplitudes were measured using fALFF within the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) bands. Repeated-measures analysis of variance was performed on fALFF within two bands and correlated with neuropsychological test scores. The significant main effects of frequency and group on fALFF differed widely across brain regions. There were more varied areas in the slow-5 band than the slow-4 band. The fALFF associated with primary disease effects was mainly distributed in the parietal lobe. Obvious frequency band and group interaction effects were observed in the left angular gyrus, left calcarine fissure and surrounding cortex, left superior cerebellum, left cuneus and right lingual gyrus. Neuropsychological tests scores were significantly correlated with the fALFF magnitude of the left cuneus and right lingual in the slow-5 band. Our results suggested that the AD continuum had abnormal amplitudes in intrinsic brain activity, and these abnormalities were frequency-dependent and mainly associated with the slow-5 band rather than the slow-4 band. This may guide the frequency choice of future rs-fMRI studies and provide new insights into the neuropathophysiology of AD.

Attention and Functional Connectivity Among Patients With Early-Stage Subcortical Ischemic Vascular Disease and Alzheimer's Disease

The current study compared attention profiles and functional connectivity of frontal regions in patients with early-stage subcortical ischemic vascular disease (SIVD) and Alzheimer's disease (AD). Twenty patients with SIVD, 32 patients with AD, and 23 subjects with normal cognition (NC) received cognition and resting-state functional MRI (rs-fMRI) evaluations. The Cognitive Abilities Screening Instrument (CASI) was used to assess global cognition, and simple attention, processing speed, divided attention, and vigilance/sustained attention were evaluated using the Digit Span Forward, Trail Making Test, Symbol Digit Modality Test, and Conners Continuous Performance Test, respectively. Voxel-based regional homogeneity (ReHo) derived from rs-fMRI data was analyzed to identify significant clusters, which were further correlated with attention profiles. Although the patients with SIVD and AD had comparable global cognitive ability, those with SIVD exhibited worse divided attention and vigilance/sustained attention than those with AD. Compared with the NC group, the patients with SIVD exhibited decreased ReHo within the right middle frontal gyrus (MFG) and left anterior cingulate gyrus (ACG), whereas the patients with AD exhibited increased ReHo within the right orbital part of frontal regions. Correlations between these three clusters with attention exhibited distinct patterns according to the dementia subtype, as did attention indices with significance in predicting global cognition. In summary, our study suggested that worse attention performance was associated with functional disconnection within the frontal regions among patients with SIVD than in those with AD. Frontal functional disconnection may underlie the pathogenesis responsible for defective divided attention, vigilance/sustained attention, and notable within-group variations identified in SIVD.

Dysfunctional Dynamics of Intra- and Inter-network Connectivity in Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is characterized by the transient fluctuating cognition and recurrent visual hallucinations, which may be caused by disorders of the intrinsic brain network dynamics. However, little is known regarding the dynamic features of the brain network behind these symptoms in DLB. In the present study, the intra- and inter-brain network dynamics were explored on a time scale in 17 DLB and 20 healthy controls (HC) applying a sliding-window method followed by k-means clustering analysis. To further evaluate the impact of network dynamics on brain performance, the local and global efficiency of the brain network was calculated. Compared with HC, the dynamic functional connectivity variation matrix in DLB patients was represented by a mixed change of intra-network increase and inter-network decrease. DLB patients devoted more time to a negative connectivity pattern, which represents a state of functional separation. Furthermore, the local efficiency of DLB patients was significantly lower compared with HC. These observations indicate an altered dynamic variability and disorders to the time allocation of state sequences in DLB, which might result in a disturbance of the intricate brain network dynamic properties, thereby leading to a lack of integration and flexibility and an ineffective brain function. In conclusion, dynamic functional connectivity analysis could identify differences between DLB and HC, providing evidences for DLB diagnosis and contributing to the understanding of the widespread clinical features and complex treatment strategies in DLB patients.

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

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

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

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

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

Docosahexaenoic Acid Increases the Potency of Soluble Epoxide Hydrolase Inhibitor in Alleviating Streptozotocin-Induced Alzheimer's Disease-Like Complications of Diabetes

Diabetes is a risk factor for Alzheimer's disease and it is associated with significant memory loss. In the present study, we hypothesized that the soluble epoxide hydrolase (sEH) inhibitor N-[1-(1-oxopropyl)-4-piperidinyl]-N'-[4-(trifluoromethoxy)phenyl)-urea (also known as TPPU) could alleviate diabetes-aggravated Alzheimer's disease-like symptoms by improving memory and cognition, and reducing the oxidative stress and inflammation associated with this condition. Also, we evaluated the effect of edaravone, an antioxidant on diabetes-induced Alzheimer's-like complications and the additive effect of docosahexaenoic acid (DHA) on the efficacy of TPPU. Diabetes was induced in male Sprague-Dawley rats by intraperitoneally administering streptozotocin (STZ). Six weeks after induction of diabetes, animals were either treated with vehicle, edaravone (3 or 10 mg/kg), TPPU (1 mg/kg) or TPPU (1 mg/kg) + DHA (100 mg/kg) for 2 weeks. The results demonstrate that the treatments increased the memory response of diabetic rats, in comparison to untreated diabetic rats. Indeed, DHA + TPPU were more effective than TPPU alone in reducing the symptoms monitored. All drug treatments reduced oxidative stress and minimized inflammation in the brain of diabetic rats. Expression of the amyloid precursor protein (APP) was increased in the brain of diabetic rats. Treatment with edaravone (10 mg/kg), TPPU or TPPU + DHA minimized the level of APP. The activity of acetylcholinesterase (AChE) which metabolizes acetylcholine was increased in the brain of diabetic rats. All the treatments except edaravone (3 mg/kg) were effective in decreasing the activity of AChE and TPPU + DHA was more efficacious than TPPU alone. Intriguingly, the histological changes in hippocampus after treatment with TPPU + DHA showed significant protection of neurons against STZ-induced neuronal damage. Overall, we found that DHA improved the efficacy of TPPU in increasing neuronal survival and memory, decreasing oxidative stress and inflammation possibly by stabilizing anti-inflammatory and neuroprotective epoxides of DHA. In the future, further evaluating the detailed mechanisms of action of sEH inhibitor and DHA could help to develop a strategy for the management of Alzheimer's-like complications in diabetes.

Role of Hypothalamic-Pituitary-Adrenal Axis, Hypothalamic-Pituitary-Gonadal Axis and Insulin Signaling in the Pathophysiology of Alzheimer's Disease

Alzheimer's disease (AD), the commonest progressive neurodegenerative disorder of the brain, is clinically characterized by the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. Recent studies suggest a relationship between the endocrinal dysregulation and the neuronal loss during the AD pathology. Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and hypothalamic-pituitary-gonadal (HPG) axis regulating circulating levels of glucocorticoid hormones has been implicated in the pathophysiology of AD. Likewise, dysregulated insulin signaling, impaired glucose uptake and insulin resistance are some of the prime factors in the onset/progression of AD. In this review, we have discussed the changes in HPA and HPG axes, implicated insulin resistance/signaling and glucose regulation during the onset/progression of AD. Therefore, simultaneous detection of these endocrinal markers in the early or presymptomatic stages may help in the early diagnosis of AD. This evidence for implicated endocrinal functions supports the fact that modulation of endocrinal pathways can be used as therapeutic targets for AD. Future studies need to determine how the induction or inhibition of endocrinal targets could be used for predictable neuroprotection in AD therapies.

Regional Dynamics of the Resting Brain in Amyotrophic Lateral Sclerosis Using Fractional Amplitude of Low-Frequency Fluctuations and Regional Homogeneity Analyses

Resting-state functional magnetic resonance imaging has been playing an important role in the study of amyotrophic lateral sclerosis (ALS). Although functional connectivity is widely studied, the patterns of spontaneous neural activity of the resting brain are important mechanisms that have been used recently to study a variety of conditions but remain less explored in ALS. Here we have used fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) to study the regional dynamics of the resting brain of nondemented ALS patients compared with healthy controls. As expected, we found the sensorimotor network with changes in fALFF and ReHo, and also found the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) altered and the cerebellum, although no structural changes between ALS patients and controls were reported in the regions with fALFF and ReHo changes. We show an altered pattern in the spontaneous low-frequency oscillations that is not confined to the motor areas and reveal a more widespread involvement of nonmotor regions, including those responsible for cognition.

Structural and Functional Magnetic Resonance Imaging of Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia after Alzheimer's disease (AD). Although diagnosis may be challenging, there is increasing evidence that the use of biomarkers according to 2017 revised criteria for diagnosis and management of dementia with Lewy bodies can increase diagnostic accuracy. Apart from nuclear medicine techniques, various magnetic resonance imaging (MRI) techniques have been utilized in attempt to enhance diagnostic accuracy. This chapter reviews structural, functional and diffusion MRI studies in DLB cohorts being compared to healthy controls, AD or dementia in Parkinson's disease (PDD). We also included relatively new MRI methods that may have potential to identify early DLB subjects and aim at examining brain iron and neuromelanin.

Quantitative electroencephalography as a marker of cognitive fluctuations in dementia with Lewy bodies and an aid to differential diagnosis

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EEG slowing was evident in dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) and less in Alzheimer’s disease (AD) patients compared to controls.

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Dominant rhythm variability was larger in AD but only correlated with cognitive fluctuations in DLB.

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QEEG variables classified DLB and AD patients with high sensitivity and specificity.

Objective

We investigated for quantitative EEG (QEEG) differences between Alzheimer’s disease (AD), dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD) patients and healthy controls, and for QEEG signatures of cognitive fluctuations (CFs) in DLB.

Methods

We analysed eyes-closed, resting state EEGs from 18 AD, 17 DLB and 17 PDD patients with mild dementia, and 21 age-matched controls. Measures included spectral power, dominant frequency (DF), frequency prevalence (FP), and temporal DF variability (DFV), within defined EEG frequency bands and cortical regions.

Results

DLB and PDD patients showed a leftward shift in the power spectrum and DF. AD patients showed greater DFV compared to the other groups. In DLB patients only, greater DFV and EEG slowing were correlated with CFs, measured by the clinician assessment of fluctuations (CAF) scale. The diagnostic accuracy of the QEEG measures was 94% (90.4–97.9%), with 92.26% (80.4–100%) sensitivity and 83.3% (73.6–93%) specificity.

Conclusion

Although greater DFV was only shown in the AD group, within the DLB group a positive DFV – CF correlation was found. QEEG measures could classify DLB and AD patients with high sensitivity and specificity.

Significance

The findings add to an expanding literature suggesting that EEG is a viable diagnostic and symptom biomarker in dementia, particularly DLB.

Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker?

Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity. While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others. For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias. Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.

Functional connectivity in dementia with Lewy bodies: A within- and between-network analysis

Dementia with Lewy bodies (DLB) is a common form of dementia and is characterized by cognitive fluctuations, visual hallucinations, and Parkinsonism. The phenotypic expression of the disease may, in part, relate to alterations in functional connectivity within and between brain networks. This resting-state study sought to clarify this in DLB, how networks differed from Alzheimer's disease (AD), and whether they were related to clinical symptoms in DLB. Resting-state networks were estimated using independent component analysis. We investigated functional connectivity changes in 31 DLB patients compared to 31 healthy controls and a disease comparator group of 29 AD patients using dual regression and FSLNets. Within-network connectivity was generally decreased in DLB compared to controls, mainly in motor, temporal, and frontal networks. Between-network connectivity was mainly intact; only the connection between a frontal and a temporal network showed increased connectivity in DLB. Differences between AD and DLB were subtle and we did not find any significant correlations with the severity of clinical symptoms in DLB. This study emphasizes the importance of reduced connectivity within motor, frontal, and temporal networks in DLB with relative sparing of the default mode network. The lack of significant correlations between connectivity measures and clinical scores indicates that the observed reduced connectivity within these networks might be related to the presence, but not to the severity of motor and cognitive impairment in DLB patients. Furthermore, our results suggest that AD and DLB may show more similarities than differences in patients with mild disease.

Insulin signaling: An opportunistic target to minify the risk of Alzheimer's disease

Alzheimer's disease (AD) is progressive neurodegenerative disorder characterized by accumulation of senile plaques, neurofibrillary tangles (NFT) and neurodegeneration. The diabetes mellitus (DM) is one of the risk factors for AD pathogenesis by impairment in insulin signaling and glucose metabolism in central as well as peripheral system. Insulin resistance, impaired glucose and lipid metabolism are leading to the Aβ (Aβ) aggregation, Tau phosphorylation, mitochondrial dysfunction, oxidative stress, protein misfolding, memory impairment and also mark over Aβ transport through central to peripheral and vice versa. Several pathways, like enzymatic degradation of Aβ, forkhead box protein O1 (FOXO) signaling, insulin signaling shared common pathological mechanism for both AD and DM. Recent evidence showed that hyperinsulinemia and hyperglycemia affect the onset and progression of AD differently. Some researchers have suggested that hyperglycemia influences vascular tone, while hyperinsulinemia may underlie mitochondrial deficit. The objective of this review is to determine whether existing evidence supports the concept that impairment in insulin signaling and glucose metabolism play an important role in pathogenesis of AD. In the first part of this review, we tried to explain the interconnecting link between AD and DM, whereas the second part includes more information on insulin resistance and its involvement in AD pathogenesis. In the final part of this review, we have focused more toward the AD treatment by targeting insulin signaling like anti-diabetic, antioxidant, nutraceuticals and dietary supplements. To date, more researches should be done in this field in order to explore the pathways in insulin signaling, which might ameliorate the treatment options and reduce the risk of AD due to DM.