Low-frequency oscillations in the brain show differential regional associations with severity of cerebral small vessel disease: a systematic review

Background

Cerebral small vessel disease (cSVD) is associated with endothelial dysfunction but the pathophysiology is poorly understood. Low-frequency oscillations (LFOs) in the BOLD signal partly reflect cerebrovascular function and have the potential to identify endothelial dysfunction in cSVD. A systematic review was performed to assess the reported relationships between imaging markers of cSVD and LFOs.

Methods

Medline and EMBASE were searched for original studies reporting an association between LFOs and STRIVE-defined imaging markers of cSVD, including: white matter hyperintensities (WMH), enlarged perivascular spaces, lacunes, CADASIL, and cerebral microbleeds, from inception to September 1, 2022. Variations in LFOs were extracted, where available, on a global, tissue-specific, or regional level, in addition to participant demographics, data acquisition, methods of analysis, and study quality. Where a formal meta-analysis was not possible, differences in the number of studies reporting LFO magnitude by presence or severity of cSVD were determined by sign test.

Results

15 studies were included from 841 titles. Studies varied in quality, acquisition parameters, and in method of analysis. Amplitude of low-frequency fluctuation (ALFF) in resting state fMRI was most commonly assessed (12 studies). Across 15 studies with differing markers of cSVD (9 with WMH; 1 with cerebral microbleeds; 1 with lacunar infarcts; 1 with CADASIL; 3 with multiple markers), LFOs in patients with cSVD were decreased in the posterior cortex (22 of 32 occurrences across all studies, p = 0.05), increased in the deep grey nuclei (7 of 7 occurrences across all studies, p = 0.016), and potentially increased in the temporal lobes (9 of 11 occurrences across all studies, p = 0.065).

Conclusion

Despite limited consensus on the optimal acquisition and analysis methods, there was reasonably consistent regional variation in LFO magnitude by severity of cSVD markers, supporting its potential as a novel index of endothelial dysfunction. We propose a consistent approach to measuring LFOs to characterise targetable mechanisms underlying cSVD.

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.

Cognitive Versus Hemorrhagic Onset in Cerebral Amyloid Angiopathy: Neuroimaging Features

BACKGROUND

Intracerebral hemorrhage and cognitive decline are typical clinical presentations of cerebral amyloid angiopathy (CAA).

OBJECTIVE

To determine whether magnetic resonance imaging (MRI) features differ between CAA with hemorrhagic versus cognitive onset.

METHODS

In this retrospective study, sixty-one patients with CAA were classified by onset presentation of the disease: hemorrhage (n = 31) or cognitive decline (n = 30). The two groups were compared for MRI markers of small vessel disease, namely cerebral microbleeds (CMBs), cortical superficial siderosis, white matter hyperintensities (WMHs), enlarged perivascular spaces, cortical microinfarcts, and visual rating scales for cortical atrophy. In the patients with cognitive onset, further exploratory analyses investigated MRI markers according to cerebrospinal fluid (CSF) and neuropsychological profiles.

RESULTS

Patients with cognitive onset showed a higher prevalence of CMBs (p < 0.001), particularly in temporal (p = 0.015) and insular (p = 0.002) lobes, and a higher prevalence of WMHs (p = 0.012). Within the cognitive onset group, 12 out of 16 (75%) patients had an Alzheimer's disease (AD) CSF profile but did not differ in MRI markers from those without AD pathology. Patients with cognitive onset displayed a multidomain profile in 16 out of 23 (70%) cases; patients with this profile showed increased WMHs and CMBs in parietal lobes compared with the amnestic group (p = 0.002) and dysexecutive group (p = 0.032), respectively.

CONCLUSION

Higher burdens of WMHs and CMBs, especially in temporal and insular lobes, are associated with the cognitive onset of CAA. MRI markers could help to shed light on the clinical heterogeneity of the CAA spectrum and its underlying mechanisms.

Comparing the Effectiveness of Brain Structural Imaging, Resting-state fMRI, and Naturalistic fMRI in Recognizing Social Anxiety Disorder in Children and Adolescents

Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD.

Risk factors, neuroimaging correlates and prognosis of the motoric cognitive risk syndrome: a population-based comparison with mild cognitive impairment

Background and purpose

This study was undertaken to compare risk factors, neuroimaging characteristics and prognosis between two clinical prodromes of dementia, namely, the motoric cognitive risk syndrome (MCRS) and mild cognitive impairment (MCI).

Methods

Between 2009 and 2015, dementia‐free participants of the population‐based Rotterdam Study were classified with a dementia prodrome if they had subjective cognitive complaints and scored >1 SD below the population mean of gait speed (MCRS) or >1.5 SD below the population mean of cognitive test scores (MCI). Using multinomial logistic regression models, we determined cross‐sectional associations of risk factors and structural neuroimaging markers with MCRS and MCI, followed by subdistribution hazard models, to determine risk of incident dementia until 2016.

Results

Of 3025 included participants (mean age = 70.4 years, 54.7% women), 231 had MCRS (7.6%), 132 had MCI (4.4%), and 62 (2.0%) fulfilled criteria for both. Although many risk factors were shared, a higher body mass index predisposed to MCRS, whereas male sex and hypercholesterolemia were associated with MCI only. Gray matter volumes, hippocampal volumes, white matter hyperintensities, and structural white matter integrity were worse in both MCRS and MCI. During a mean follow‐up of 3.9 years, 71 individuals developed dementia and 200 died. Five‐year cumulative risk of dementia was 7.0% (2.5%–11.5%) for individuals with MCRS, versus 13.3% (5.8%–20.8%) with MCI and only 2.3% (1.5%–3.1%) in unaffected individuals.

Conclusions

MCRS is associated with imaging markers of neurodegeneration and risk of dementia, even in the absence of MCI, highlighting the potential of motor function assessment in early risk stratification for dementia.

Neuroimaging Markers of Cerebral Small Vessel Disease on Hemorrhagic Transformation and Functional Outcome After Intravenous Thrombolysis in Patients With Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Objective: The aim of this study was to perform a systematic review and meta-analysis to assess whether cerebral small vessel disease (CSVD) on neuroimaging of patients with acute ischemic stroke (AIS) treated with intravenous thrombolysis (IVT) is associated with an increased risk of hemorrhagic transformation (HT), symptomatic intracranial hemorrhage (sICH), and poor functional outcome (PFO). Methods: A thorough search of several databases was carried out to identify relevant studies up to December 2020. We included studies of patients with AIS and neuroimaging markers of CSVD treated with IVT. The primary outcome was HT, and the secondary outcomes were sICH and 3-month PFO. The quality of the studies involved was evaluated using the Newcastle-Ottawa Scale (NOS). The meta-analysis with the fixed effects model was performed. Results: Twenty-four eligible studies (n = 9,419) were pooled in the meta-analysis. All included studies were regarded as high quality with the NOS scores of at least 6 points. The meta-analysis revealed associations between the presence of CSVD and HT, sICH, and the 3-month PFO after IVT. Compared with no CSVD, the presence of CSVD was associated with an increased risk of HT (OR: 1.81, 95% CI: 1.52-2.16), sICH (OR: 2.42, 95% CI: 1.76-3.33), and 3-month PFO (OR: 2.15, 95% CI: 1.89-2.44). For patients with AIS complicated with CSVD, compared with a CSVD score of 0-1, a CSVD score of 2-4 was associated with an increased risk of HT (OR: 3.10, 95% CI: 1.67-5.77), sICH (OR: 2.86, 95% CI: 1.26-6.49), and 3-month PFO (OR: 4.58, 95% CI: 2.97-7.06). Conclusion: Patients with AIS complicated with neuroimaging markers of CSVD are at increased risk of HT and 3-month PFO after IVT. However, it is still necessary to clarify the exact role of CSVD in the occurrence, development, and prognosis of AIS. Systematic Review Registration: www.ClinicalTrials.gov, identifier CRD4202123 3900.

Biomarkers in Sporadic and Familial Alzheimer's Disease

Most forms of Alzheimer's disease (AD) are sporadic (sAD) or inherited in a non-Mendelian fashion, and less than 1% of cases are autosomal-dominant. Forms of sAD do not exhibit familial aggregation and are characterized by complex genetic and environmental interactions. Recently, the expansion of genomic methodologies, in association with substantially larger combined cohorts, has resulted in various genome-wide association studies that have identified several novel genetic associations of AD. Currently, the most effective methods for establishing the diagnosis of AD are defined by multi-modal pathways, starting with clinical and neuropsychological assessment, cerebrospinal fluid (CSF) analysis, and brain-imaging procedures, all of which have significant cost- and access-to-care barriers. Consequently, research efforts have focused on the development and validation of non-invasive and generalizable blood-based biomarkers. Among the modalities conceptualized by the systems biology paradigm and utilized in the "exploratory biomarker discovery arena", proteome analysis has received the most attention. However, metabolomics, lipidomics, transcriptomics, and epigenomics have recently become key modalities in the search for AD biomarkers. Interestingly, biomarker changes for familial AD (fAD), in many but not all cases, seem similar to those for sAD. The integration of neurogenetics with systems biology/physiology-based strategies and high-throughput technologies for molecular profiling is expected to help identify the causes, mechanisms, and biomarkers associated with the various forms of AD. Moreover, in order to hypothesize the dynamic trajectories of biomarkers through disease stages and elucidate the mechanisms of biomarker alterations, updated and more sophisticated theoretical models have been proposed for both sAD and fAD.

The role of sexual abuse on functional neuroimaging markers associated with major depressive disorder

OBJECTIVES

Victims of child sexual abuse can develop depression and other mental health conditions that follow them well into adulthood. This study aimed to clarify the role of sexual abuse (SA) on functional imaging markers associated with MDD.

METHODS

Thirty-seven patients with MDD only; and 13 patients with both MDD and SA and 43 healthy controls performed emotional attention shifting tasks during fMRI session. Clinical diagnoses were made by consultant psychiatrists based on the DSM-IV-TR criteria and diagnoses were confirmed using SCID-I. Magnetic resonance images were obtained with a Philips Achieva 3 Tesla MRI scanner. Short form childhood trauma questionnaire, Hamilton Rating Scale for Depression and Beck's Depression Inventory were also employed. Data were analysed with Statistical Parametric Mapping 8 (SPM8).

RESULTS

Using the contrast judgment of emotion minus judgment of geometry following emotional neutral stimuli, patients with MDD showed significantly reduced activation in comparison to healthy controls in the area of the right fusiform gyrus. With the contrast judgment of emotion minus judgment of geometry following emotional negative stimuli, participants with MDD and SA showed significantly higher activation in the area of the left inferior parietal lobe in comparison to participants with MDD without SA.

CONCLUSIONS

The history of sexual abuse affects functional neuroimaging markers associated with major depressive disorder.