Role of Imaging Genetics in Alzheimer's Disease: A Systematic Review and Current Update

BACKGROUND

Alzheimer's disease is a neurodegenerative disorder characterized by severe cognitive, behavioral, and psychological symptoms, such as dementia, cognitive decline, apathy, and depression. There are no accurate methods to diagnose the disease or proper therapeutic interventions to treat AD. Therefore, there is a need for novel diagnostic methods and markers to identify AD efficiently before its onset. Recently, there has been a rise in the use of imaging techniques like Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) as diagnostic approaches in detecting the structural and functional changes in the brain, which help in the early and accurate diagnosis of AD. In addition, these changes in the brain have been reported to be affected by variations in genes involved in different pathways involved in the pathophysiology of AD.

METHODOLOGY

A literature review was carried out to identify studies that reported the association of genetic variants with structural and functional changes in the brain in AD patients. Databases like PubMed, Google Scholar, and Web of Science were accessed to retrieve relevant studies. Keywords like 'fMRI', 'Alzheimer's', 'SNP', and 'imaging' were used, and the studies were screened using different inclusion and exclusion criteria.

RESULTS

15 studies that found an association of genetic variations with structural and functional changes in the brain were retrieved from the literature. Based on this, 33 genes were identified to play a role in the development of disease. These genes were mainly involved in neurogenesis, cell proliferation, neural differentiation, inflammation and apoptosis. Few genes like FAS, TOM40, APOE, TRIB3 and SIRT1 were found to have a high association with AD. In addition, other genes that could be potential candidates were also identified.

CONCLUSION

Imaging genetics is a powerful tool in diagnosing and predicting AD and has the potential to identify genetic biomarkers and endophenotypes associated with the development of the disorder.

Task fMRI studies investigating inhibitory control in patients with obsessive-compulsive disorder and eating disorders: A comparative meta-analysis

BACKGROUND

Obsessive-compulsive disorder (OCD) and eating disorders (EDs) share similarities in terms of clinical characteristics and deficits in inhibitory control.

OBJECTIVE

To investigate whether inhibitory control could serve as a common behavioural phenotype between OCD and EDs and whether it might be underpinned by shared and/or distinct neural signatures.

METHOD

We performed a quantitative meta-analysis of brain function abnormalities during the inhibitory control task-based functional Magnetic Resonance Imaging (fMRI) scan across patients with OCD and EDs using seed-based d mapping (SDM).

RESULTS

The meta-analysis included sixteen OCD fMRI studies and ten EDs fMRI studies. And findings revealed that patients with OCD showed hypoactivation relative to healthy controls and patients with EDs in the anterior cingulate cortex, while compared to healthy controls and patients with OCD, patients with EDs showed hypoactivation in the right insula.

CONCLUSIONS

Patients with OCD and EDs are inclined to exhibit impaired inhibitory control, which may be attributed to different abnormal patterns of neural activation.

Two different brain networks underlying picture naming with familiar pre-existing native words and new vocabulary

The present research used fMRI to longitudinally investigate the impact of learning new vocabulary on the activation pattern of the language control network by measuring BOLD signal changes during picture naming tasks with familiar pre-existing native words (old words) and new vocabulary. Nineteen healthy participants successfully learned new synonyms for already known Spanish words, and they performed a picture naming task using the old words and the new words immediately after learning and two weeks after learning. The results showed that naming with old words, compared to naming with newly learned words, produced activations in a cortical network involving frontal and parietal regions, whereas the opposite contrast showed activation in a broader cortical/subcortical network, including the SMA/ACC, the hippocampus, and the midbrain. These two networks are maintained two weeks after learning. These results suggest that the language control network can be separated into two functional circuits for diverse cognitive purposes.

Disentangling craving- and valence-related brain responses to smoking cues in individuals with nicotine use disorder

Tobacco smoking is one of the leading causes of preventable death and disease worldwide. Most smokers want to quit, but relapse rates are high. To improve current smoking cessation treatments, a better understanding of the underlying mechanisms of nicotine dependence and related craving behaviour is needed. Studies on cue‐driven cigarette craving have been a particularly useful tool for investigating the neural mechanisms of drug craving. Here, functional neuroimaging studies in humans have identified a core network of craving‐related brain responses to smoking cues that comprises of amygdala, anterior cingulate cortex, orbitofrontal cortex, posterior cingulate cortex and ventral striatum. However, most functional Magnetic Resonance Imaging (fMRI) cue‐reactivity studies do not adjust their stimuli for emotional valence, a factor assumed to confound craving‐related brain responses to smoking cues. Here, we investigated the influence of emotional valence on key addiction brain areas by disentangling craving‐ and valence‐related brain responses with parametric modulators in 32 smokers. For one of the suggested key regions for addiction, the amygdala, we observed significantly stronger brain responses to the valence aspect of the presented images than to the craving aspect. Our results emphasize the need for carefully selecting stimulus material for cue‐reactivity paradigms, in particular with respect to emotional valence. Further, they can help designing future research on teasing apart the diverse psychological dimensions that comprise nicotine dependence and, therefore, can lead to a more precise mapping of craving‐associated brain areas, an important step towards more tailored smoking cessation treatments.

Increased Amygdala Activations during the Emotional Experience of Death-Related Pictures in Complicated Grief: An fMRI Study

Complicated grief (CG) is associated with alterations in various components of emotional processing. The main aim of this study was to identify brain activations in individuals diagnosed with CG while they were observing positive, negative, and death-related pictures. The participants included 19 individuals with CG and 19 healthy non-bereaved (NB) individuals. Functional magnetic resonance imaging (fMRI) scans were obtained during an emotional experience task. The perception of death-related pictures differed between the CG group and the NB group, with a greater activation in the former of the amygdala, putamen, hypothalamus, middle frontal gyrus, and anterior cingulate cortex. Amygdala and putamen activations were significantly correlated with Texas Revised Inventory of Grief scores in the CG group, suggesting that the higher level of grief in this group was associated with a greater activation in both brain areas while watching death-related pictures. A significant interaction between image type and group was observed in the amygdala, midbrain, periaqueductal gray, cerebellum, and hippocampus, largely driven by the greater activation of these areas in the CG group when watching death-related pictures and the lower activation when watching positive-valence pictures. In this study, individuals with CG showed significantly distinct brain activations in response to different emotional images.

Identification of brain regions associated with working memory deficit in schizophrenia

Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory.

Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected.

Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients.

Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

Brain Mechanisms of Anticipated Painful Movements and Their Modulation by Manual Therapy in Chronic Low Back Pain

Heightened anticipation and fear of movement-related pain has been linked to detrimental fear-avoidance behavior in chronic low back pain (cLBP). Spinal manipulative therapy (SMT) has been proposed to work partly by exposing patients to nonharmful but forceful mobilization of the painful joint, thereby disrupting the relationship among pain anticipation, fear, and movement. Here, we investigated the brain processes underpinning pain anticipation and fear of movement in cLBP, and their modulation by SMT, using functional magnetic resonance imaging. Fifteen cLBP patients and 16 healthy control (HC) subjects were scanned while observing and rating video clips depicting back-straining or neutral physical exercises, which they knew they would have to perform at the end of the visit. This task was repeated after a single session of spinal manipulation (cLBP and HC group) or mobilization (cLBP group only), in separate visits. Compared with HC subjects, cLBP patients reported higher expected pain and fear of performing the observed exercises. These ratings, along with clinical pain, were reduced by SMT. Moreover, cLBP, relative to HC subjects, demonstrated higher blood oxygen level-dependent signal in brain circuitry that has previously been implicated in salience, social cognition, and mentalizing, while observing back straining compared with neutral exercises. The engagement of this circuitry was reduced after SMT, and especially the spinal manipulation session, proportionally to the magnitude of SMT-induced reduction in anticipated pain and fear. This study sheds light on the brain processing of anticipated pain and fear of back-straining movement in cLBP, and suggests that SMT may reduce cognitive and affective-motivational aspects of fear-avoidance behavior, along with corresponding brain processes. PERSPECTIVE: This study of cLBP patients investigated how SMT affects clinical pain, expected pain, and fear of physical exercises. The results indicate that one of the mechanisms of SMT may be to reduce pain expectancy, fear of movement, and associated brain responses.

A flexible graphical model for multi-modal parcellation of the cortex

Advances in neuroimaging have provided a tremendous amount of in-vivo information on the brain's organisation. Its anatomy and cortical organisation can be investigated from the point of view of several imaging modalities, many of which have been studied for mapping functionally specialised cortical areas. There is strong evidence that a single modality is not sufficient to fully identify the brain's cortical organisation. Combining multiple modalities in the same parcellation task has the potential to provide more accurate and robust subdivisions of the cortex. Nonetheless, existing brain parcellation methods are typically developed and tested on single modalities using a specific type of information. In this paper, we propose Graph-based Multi-modal Parcellation (GraMPa), an iterative framework designed to handle the large variety of available input modalities to tackle the multi-modal parcellation task. At each iteration, we compute a set of parcellations from different modalities and fuse them based on their local reliabilities. The fused parcellation is used to initialise the next iteration, forcing the parcellations to converge towards a set of mutually informed modality specific parcellations, where correspondences are established. We explore two different multi-modal configurations for group-wise parcellation using resting-state fMRI, diffusion MRI tractography, myelin maps and task fMRI. Quantitative and qualitative results on the Human Connectome Project database show that integrating multi-modal information yields a stronger agreement with well established atlases and more robust connectivity networks that provide a better representation of the population.

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

PURPOSE

Multiple computational studies have demonstrated that essentially all current analytical approaches to determine effective connectivity perform poorly when applied to synthetic functional Magnetic Resonance Imaging (fMRI) datasets. In this study, we take a theoretical approach to investigate the potential factors facilitating and hindering effective connectivity research in fMRI.

MATERIALS AND METHODS

In this work, we perform a simulation study with use of Dynamic Causal Modeling generative model in order to gain new insights on the influence of factors such as the slow hemodynamic response, mixed signals in the network and short time series, on the effective connectivity estimation in fMRI studies.

RESULTS

First, we perform a Linear Discriminant Analysis study and find that not the hemodynamics itself but mixed signals in the neuronal networks are detrimental to the signatures of distinct connectivity patterns. This result suggests that for statistical methods (which do not involve lagged signals), deconvolving the BOLD responses is not necessary, but at the same time, functional parcellation into Regions of Interest (ROIs) is essential. Second, we study the impact of hemodynamic variability on the inference with use of lagged methods. We find that the local hemodynamic variability provide with an upper bound on the success rate of the lagged methods. Furthermore, we demonstrate that upsampling the data to TRs lower than the TRs in state-of-the-art datasets does not influence the performance of the lagged methods.

CONCLUSIONS

Factors such as background scale-free noise and hemodynamic variability have a major impact on the performance of methods for effective connectivity research in functional Magnetic Resonance Imaging.

Recovery of post stroke proximal arm function, driven by complex neuroplastic bilateral brain activation patterns and predicted by baseline motor dysfunction severity

Objectives: Neuroplastic changes that drive recovery of shoulder/elbow function after stroke have been poorly understood. The purpose of this study was to determine the relationship between neuroplastic brain changes related to shoulder/elbow movement control in response to treatment and recovery of arm motor function in chronic stroke survivors.Methods: Twenty-three chronic stroke survivors were treated with 12 weeks of arm rehabilitation. Outcome measures included functional Magnetic Resonance Imaging (fMRI) for the shoulder/elbow components of reach and a skilled motor function test (Arm Motor Abilities Test, AMAT), collected before and after treatment.Results: We observed two patterns of neuroplastic changes that were associated with gains in motor function: decreased or increased task-related brain activation. Those with significantly better motor function at baseline exhibited a decrease in brain activation in response to treatment, evident in the ipsilesional primary motor and contralesional supplementary motor regions; in contrast, those with greater baseline motor impairment, exhibited increased brain activation in response to treatment. There was a linear relationship between greater functional gain (AMAT) and increased activation in bilateral primary motor, contralesional primary and secondary sensory regions, and contralesional lateral premotor area, after adjusting for baseline AMAT, age, and time since stroke.Conclusions: Recovery of functional reach involves recruitment of several contralesional and bilateral primary motor regions. In response to intensive therapy, the direction of functional brain change (i.e., increase or decrease in task-related brain recruitment) for shoulder/elbow reach components depends on baseline level of motor function and may represent either different phases of recovery or different patterns of neuroplasticity that drive functional recovery.

Optical Coherence Tomography angiography reveals laminar microvascular hemodynamics in the rat somatosensory cortex during activation

The BOLD (blood-oxygen-level dependent) fMRI (functional Magnetic Resonance Imaging) signal is shaped, in part, by changes in red blood cell (RBC) content and flow across vascular compartments over time. These complex dynamics have been challenging to characterize directly due to a lack of appropriate imaging modalities. In this study, making use of infrared light scattering from RBCs, depth-resolved Optical Coherence Tomography (OCT) angiography was applied to image laminar functional hyperemia in the rat somatosensory cortex. After defining and validating depth-specific metrics for changes in RBC content and speed, laminar hemodynamic responses in microvasculature up to cortical depths of >1mm were measured during a forepaw stimulus. The results provide a comprehensive picture of when and where changes in RBC content and speed occur during and immediately following cortical activation. In summary, the earliest and largest microvascular RBC content changes occurred in the middle cortical layers, while post-stimulus undershoots were most prominent superficially. These laminar variations in positive and negative responses paralleled known distributions of excitatory and inhibitory synapses, suggesting neuronal underpinnings. Additionally, the RBC speed response consistently returned to baseline more promptly than RBC content after the stimulus across cortical layers, supporting a "flow-volume mismatch" of hemodynamic origin.

How the brain heals emotional wounds: the functional neuroanatomy of forgiveness

In life, everyone goes through hurtful events caused by significant others: a deceiving friend, a betraying partner, or an unjustly blaming parent. In response to painful emotions, individuals may react with anger, hostility, and the desire for revenge. As an alternative, they may decide to forgive the wrongdoer and relinquish resentment. In the present study, we examined the brain correlates of forgiveness using functional Magnetic Resonance Imaging (fMRI). Healthy participants were induced to imagine social scenarios that described emotionally hurtful events followed by the indication to either forgive the imagined offenders, or harbor a grudge toward them. Subjects rated their imaginative skills, levels of anger, frustration, and/or relief when imagining negative events as well as following forgiveness. Forgiveness was associated with positive emotional states as compared to unforgiveness. Granting forgiveness was associated with activations in a brain network involved in theory of mind, empathy, and the regulation of affect through cognition, which comprised the precuneus, right inferior parietal regions, and the dorsolateral prefrontal cortex. Our results uncovered the neuronal basis of reappraisal-driven forgiveness, and extend extant data on emotional regulation to the resolution of anger and resentment following negative interpersonal events.

Lack of association between genetic polymorphisms in ROBO1, MRPL19/C2ORF3 and THEM2 with developmental dyslexia

Developmental Dyslexia (DD) is a heritable, complex genetic disorder characterized by specific impairment in reading and writing ability that is substantially below the expected reading ability given the person's chronological age, measured intelligence and age-appropriate education. More than ten susceptible genes have been identified for DD. A Single Nucleotide Polymorphism (SNP) of these genes was found to be associated with various phenotypes of DD. To identify the role of SNPs of four candidate genes namely, MRPL19/C2ORF3, ROBO1 and THEM2 in an Indian population, we genotyped eight SNPs of these genes in 157 children with DD and 212 normal readers using a MassARRAY technique with a MALDI-TOF MS analyzer. Power analysis of some of these SNPs showed >80% of power. Chi-square test, Odds Ratios (ORs), 95% Confidence Intervals (CIs) and Bonferroni's correction were applied to identify the significance of the genotyped SNPs and haplotypes. Our study failed to show any association of SNPs and haplotypes of these genes with DD in an Indian population.

An Integrative Bayesian Modeling Approach to Imaging Genetics

In this paper we present a Bayesian hierarchical modeling approach for imaging genetics, where the interest lies in linking brain connectivity across multiple individuals to their genetic information. We have available data from a functional magnetic resonance (fMRI) study on schizophrenia. Our goals are to identify brain regions of interest (ROIs) with discriminating activation patterns between schizophrenic patients and healthy controls, and to relate the ROIs' activations with available genetic information from single nucleotide polymorphisms (SNPs) on the subjects. For this task we develop a hierarchical mixture model that includes several innovative characteristics: it incorporates the selection of ROIs that discriminate the subjects into separate groups; it allows the mixture components to depend on selected covariates; it includes prior models that capture structural dependencies among the ROIs. Applied to the schizophrenia data set, the model leads to the simultaneous selection of a set of discriminatory ROIs and the relevant SNPs, together with the reconstruction of the correlation structure of the selected regions. To the best of our knowledge, our work represents the first attempt at a rigorous modeling strategy for imaging genetics data that incorporates all such features.

Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging

The default mode network is part of the brain structure that shows higher neural activity and energy consumption when one is at rest. The key regions in the default mode network are highly interconnected as conveyed by both the white matter fiber tracing and the synchrony of resting-state functional magnetic resonance imaging signals. However, the causal information flow within the default mode network is still poorly understood. The current study used the dynamic causal modeling on a resting-state fMRI data set to identify the network structure underlying the default mode network. The endogenous brain fluctuations were explicitly modeled by Fourier series at the low frequency band of 0.01-0.08Hz, and those Fourier series were set as driving inputs of the DCM models. Model comparison procedures favored a model wherein the MPFC sends information to the PCC and the bilateral inferior parietal lobule sends information to both the PCC and MPFC. Further analyses provide evidence that the endogenous connectivity might be higher in the right hemisphere than in the left hemisphere. These data provided insight into the functions of each node in the DMN, and also validate the usage of DCM on resting-state fMRI data.