Detecting change in BOLD signal between sessions for atlas-based anatomical ROIs

The Role of fMRI in Drug Development: An Update

Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).

Reduced brain activation during spoken language processing in children with developmental language disorder and children with 22q11.2 deletion syndrome

Language difficulties of children with Developmental Language Disorder (DLD) have been associated with multiple underlying factors and are still poorly understood. One way of investigating the mechanisms of DLD language problems is to compare language-related brain activation patterns of children with DLD to those of a population with similar language difficulties and a uniform etiology. Children with 22q11.2 deletion syndrome (22q11DS) constitute such a population. Here, we conducted an fMRI study, in which children (6-10yo) with DLD and 22q11DS listened to speech alternated with reversed speech. We compared language laterality and language-related brain activation levels with those of typically developing (TD) children who performed the same task. The data revealed no significant differences between groups in language lateralization, but task-related activation levels were lower in children with language impairment than in TD children in several nodes of the language network. We conclude that language impairment in children with DLD and in children with 22q11DS may involve (partially) overlapping cortical areas.

The role of fMRI in drug development

Functional magnetic resonance imaging (fMRI) has been known for over a decade to have the potential to greatly enhance the process of developing novel therapeutic drugs for prevalent health conditions. However, the use of fMRI in drug development continues to be relatively limited because of a variety of technical, biological, and strategic barriers that continue to limit progress. Here, we briefly review the roles that fMRI can have in the drug development process and the requirements it must meet to be useful in this setting. We then provide an update on our current understanding of the strengths and limitations of fMRI as a tool for drug developers and recommend activities to enhance its utility.

Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics

A data-driven hypothesis-free genome-wide association (GWA) approach in imaging genetics studies allows screening the entire genome to discover novel genes that modulate brain structure, chemistry, and function. However, a whole brain voxel-wise analysis approach in such genome-wide based imaging genetic studies can be computationally intense and also likely has low statistical power since a stringent multiple comparisons correction is needed for searching over the entire genome and brain. In imaging genetics with functional magnetic resonance imaging (fMRI) phenotypes, since many experimental paradigms activate focal regions that can be pre-specified based on a priori knowledge, reducing the voxel-wise search to single-value summary measures within a priori ROIs could prove efficient and promising. The goal of this investigation is to evaluate the sensitivity and reliability of different single-value ROI summary measures and provide guidance in future work. Four different fMRI databases were tested and comparisons across different groups (patients with schizophrenia, their siblings, vs. normal control subjects; across genotype groups) were conducted. Our results show that four of these measures, particularly those that represent values from the top most-activated voxels within an ROI are more powerful at reliably detecting group differences and generating greater effect sizes than the others.

Dynamic causal modelling of EEG and fMRI to characterize network architectures in a simple motor task

Dynamic causal modelling (DCM) has extended the understanding of brain network dynamics in a variety of functional systems. In the motor system, DCM studies based on functional magnetic resonance imaging (fMRI) or on magneto-/electroencephalography (M/EEG) have demonstrated movement-related causal information flow from secondary to primary motor areas and have provided evidence for nonlinear cross-frequency interactions among motor areas. The present study sought to investigate to what extent fMRI- and EEG-based DCM might provide complementary and synergistic insights into neuronal network dynamics. Both modalities share principal similarities in the formulation of the DCM. Thus, we hypothesized that DCM based on induced EEG responses (DCM-IR) and on fMRI would reveal congruent task-dependent network dynamics. Brain electrical (63-channel surface EEG) and Blood Oxygenation Level Dependent (BOLD) signals were recorded in separate sessions from 14 healthy participants performing simple isometric right and left hand grips. DCM-IR and DCM-fMRI were used to estimate coupling parameters modulated by right and left hand grips within a core motor network of six regions comprising bilateral primary motor cortex (M1), ventral premotor cortex (PMv) and supplementary motor area (SMA). We found that DCM-fMRI and DCM-IR similarly revealed significant grip-related increases in facilitatory coupling between SMA and M1 contralateral to the active hand. A grip-dependent interhemispheric reciprocal inhibition between M1 bilaterally was only revealed by DCM-fMRI but not by DCM-IR. Frequency-resolved coupling analysis showed that the information flow from contralateral SMA to M1 was predominantly a linear alpha-to-alpha (9-13Hz) interaction. We also detected some cross-frequency coupling from SMA to contralateral M1, i.e., between lower beta (14-21Hz) at the SMA and higher beta (22-30Hz) at M1 during right hand grip and between alpha (9-13Hz) at SMA and lower beta (14-21Hz) at M1 during left hand grip. In conclusion, the strategy of informing EEG source-space configurations with fMRI-derived coordinates, cross-validating basic connectivity maps and analysing frequency coding allows for deeper insight into the motor network architecture of the human brain. The present results provide evidence for the robustness of non-invasively measured causal information flow from secondary motor areas such as SMA towards M1 and further contribute to the validation of the methodological approach of multimodal DCM to explore human network dynamics.

A cross-linguistic evaluation of script-specific effects on fMRI lateralization in late second language readers

Behavioral and neuroimaging studies have provided evidence that reading is strongly left lateralized, and the degree of this pattern of functional lateralization can be indicative of reading competence. However, it remains unclear whether functional lateralization differs between the first (L1) and second (L2) languages in bilingual L2 readers. This question is particularly important when the particular script, or orthography, learned by the L2 readers is markedly different from their L1 script. In this study, we quantified functional lateralization in brain regions involved in visual word recognition for participants' L1 and L2 scripts, with a particular focus on the effects of L1–L2 script differences in the visual complexity and orthographic depth of the script. Two different groups of late L2 learners participated in an fMRI experiment using a visual one-back matching task: L1 readers of Japanese who learnt to read alphabetic English and L1 readers of English who learnt to read both Japanese syllabic Kana and logographic Kanji. The results showed weaker leftward lateralization in the posterior lateral occipital complex (pLOC) for logographic Kanji compared with syllabic and alphabetic scripts in both L1 and L2 readers of Kanji. When both L1 and L2 scripts were non-logographic, where symbols are mapped onto sounds, functional lateralization did not significantly differ between L1 and L2 scripts in any region, in any group. Our findings indicate that weaker leftward lateralization for logographic reading reflects greater requirement of the right hemisphere for processing visually complex logographic Kanji symbols, irrespective of whether Kanji is the readers' L1 or L2, rather than characterizing additional cognitive efforts of L2 readers. Finally, brain-behavior analysis revealed that functional lateralization for L2 visual word processing predicted L2 reading competency.

Functional neuronal network activity differs with cognitive dysfunction in childhood-onset systemic lupus erythematosus

Introduction

Neuropsychiatric manifestations are common in childhood-onset systemic lupus erythematosus (cSLE) and often include neurocognitive dysfunction (NCD). Functional magnetic resonance imaging (fMRI) can measure brain activation during tasks that invoke domains of cognitive function impaired by cSLE. This study investigates specific changes in brain function attributable to NCD in cSLE that have potential to serve as imaging biomarkers.

Methods

Formal neuropsychological testing was done to measure cognitive ability and to identify NCD. Participants performed fMRI tasks probing three cognitive domains impacted by cSLE: visuoconstructional ability (VCA), working memory, and attention. Imaging data, collected on 3-Tesla scanners, included a high-resolution T1-weighted anatomic reference image followed by a T2*-weighted whole-brain echo planar image series for each fMRI task. Brain activation using blood oxygenation level-dependent contrast was compared between cSLE patients with NCD (NCD-group, n = 7) vs. without NCD (noNCD-group, n = 14) using voxel-wise and region of interest-based analyses. The relationship of brain activation during fMRI tasks and performance in formal neuropsychological testing was assessed.

Results

Greater brain activation was observed in the noNCD-group vs. NCD-group during VCA and working memory fMRI tasks. Conversely, compared to the noNCD-group, the NCD-group showed more brain activation during the attention fMRI task. In region of interest analysis, brain activity during VCA and working memory fMRI tasks was positively associated with the participants' neuropsychological test performance. In contrast, brain activation during the attention fMRI task was negatively correlated with neuropsychological test performance. While the NCD group performed worse than the noNCD group during VCA and working memory tasks, the attention task was performed equally well by both groups.

Conclusions

NCD in patients with cSLE is characterized by differential activation of functional neuronal networks during fMRI tasks probing working memory, VCA, and attention. Results suggest a compensatory mechanism allows maintenance of attentional performance under NCD. This mechanism appears to break down for the VCA and working memory challenges presented in this study. The observation that neuronal network activation is related to the formal neuropsychological testing performance makes fMRI a candidate imaging biomarker for cSLE-associated NCD.

Functional MRI evidence for the importance of visual short-term memory in logographic reading

Logographic symbols are visually complex, and thus children's abilities for visual short-term memory (VSTM) predict their reading competence in logographic systems. In the present study, we investigated the importance of VSTM in logographic reading in adults, both behaviorally and by means of fMRI. Outside the scanner, VSTM predicted logographic Kanji reading in native Japanese adults (n=45), a finding consistent with previous observations in Japanese children. In the scanner, participants (n=15) were asked to perform a visual one-back task. For this fMRI experiment, we took advantage of the unique linguistic characteristic of the Japanese writing system, whereby syllabic Kana and logographic Kanji can share the same sound and meaning, but differ only in the complexity of their visual features. Kanji elicited greater activation than Kana in the cerebellum and two regions associated with VSTM, the lateral occipital complex and the superior intraparietal sulcus, bilaterally. The same regions elicited the highest activation during the control condition (an unfamiliar, unpronounceable script to the participants), presumably due to the increased VSTM demands for processing the control script. In addition, individual differences in VSTM performance (outside the scanner) significantly predicted blood oxygen level-dependent signal changes in the identified VSTM regions, during the Kanji and control conditions, but not during the Kana condition. VSTM appears to play an important role in reading logographic words, even in skilled adults, as evidenced at the behavioral and neural level, most likely due to the increased VSTM/visual attention demands necessary for processing complex visual features inherent in logographic symbols.

Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data

Resting-state fMRI provides a method to examine the functional network of the brain under spontaneous fluctuations. A number of studies have proposed using resting-state BOLD data to parcellate the brain into functional subunits. In this work, we present two state-of-the-art graph-based partitioning approaches, and investigate their application to the problem of brain network segmentation using resting-state fMRI. The two approaches, the normalized cut (Ncut) and the modularity detection algorithm, are also compared to the Gaussian mixture model (GMM) approach. We show that the Ncut approach performs consistently better than the modularity detection approach, and it also outperforms the GMM approach for in vivo fMRI data. Resting-state fMRI data were acquired from 43 healthy subjects, and the Ncut algorithm was used to parcellate several different cortical regions of interest. The group-wise delineation of the functional subunits based on resting-state fMRI was highly consistent with the parcellation results from two task-based fMRI studies (one with 18 subjects and the other with 20 subjects). The findings suggest that whole-brain parcellation of the cortex using resting-state fMRI is feasible, and that the Ncut algorithm provides the appropriate technique for this task.

Evaluation of atlas-based segmentation of hippocampi in healthy humans

INTRODUCTION AND AIM

Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus.

MATERIAL AND METHODS

High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/).

RESULTS

The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas.

CONCLUSION

Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis.

Spatial characterization of FMRI activation maps using invariant 3-D moment descriptors

A novel approach is proposed for quantitatively characterizing the spatial patterns of activation statistics in functional magnetic resonance imaging (fMRI) activation maps. Specifically, we propose using 3-D invariant moment descriptors, as opposed to the traditionally-employed magnitude-based features such as mean voxel statistics or percentage of activated voxels, to characterize the task-specific spatial distribution of activation statistics within a given region of interest (ROI). The proposed method is applied to real fMRI data collected from 21 healthy subjects performing previously-learned right-handed finger tapping sequences that are either externally guided (EG) by a cue or internally guided (IG)--tasks expected to incur subtle differences in motor-related cortical and subcortical ROIs. Voxel-based activation statistics contrasting EG versus rest and IG versus rest are examined in multiple manually-drawn ROIs on unwarped brain images. Analyzing the activation statistics within each ROI using the proposed 3-D invariant moment descriptors detected significant group differences between the two tasks, thus quantitatively demonstrating that the spatial distribution of activation statistics within an ROI represent an important task-related attribute of brain activation. In contrast, conventional methods that solely rely on activation statistic magnitudes and disregard spatial information showed reduced discriminability. Normally, incorporating spatial information would merely increase inter-subject variability partly due to differences in brain size and subject's orientation in the scanner. Yet, our results suggest that the proposed spatial features, which are invariant to similarity transformations, can effectively account for such inter-subject variability, while enhancing the sensitivity in detecting task-specific activation. Thus, we argue that this novel quantitative description of the "3-D texture" of activation maps provides new directions to explore for ROI-based fMRI analysis.