Objectives

Research suggests that the majority of mild traumatic brain injury (mTBI) patients exhibit both cognitive and emotional dysfunction within the first weeks of injury, followed by symptom resolution 3–6 months post-injury. The neuronal correlates of said dysfunction are difficult to detect with standard clinical neuroimaging, complicating differential diagnosis and early identification of patients who may not recover. The current study examined whether resting state functional magnetic resonance imaging (FMRI) provides objective markers of injury and predicts cognitive, emotional and somatic complaints in mTBI patients semi-acutely (< 3 weeks post-injury) and in late recovery (3–5 month) phases.

Methods

Twenty seven semi-acute mTBI patients and 26 gender, age and education matched controls were studied. Fifteen out of 27 patients returned for a follow-up visit 3–5 months post-injury. The main dependent variables were spontaneous fluctuations (temporal correlation) in the default-mode (DMN) and fronto-parietal task-related (TRN) networks as measured by FMRI.

Results

Significant differences in self-reported cognitive, emotional and somatic complaints were observed (all p < .05), despite normal clinical (T1 and T2) imaging and neuropsychological testing results. Mild TBI patients demonstrated decreased functional connectivity within the DMN and hyper-connectivity between the DMN and lateral prefrontal cortex. Measures of functional connectivity exhibited high levels of sensitivity and specificity for patient classification and predicted cognitive complaints in the semi-acute injury stage. However, no changes in functional connectivity were observed across a four month recovery period.

Conclusions

Abnormal connectivity between the DMN and frontal cortex may provide objective biomarkers of mTBI and underlie cognitive impairment.

Background

Although variations in neurometabolite concentrations occur in diverse neuropsychiatric and neurodegenerative disorders, very little is known about the nature of underlying genetic influences. The current study investigated the importance of a specific type of genetic mutation, copy number variation (CNV), for neurometabolite concentrations in a bilateral anterior cingulate voxel.

Methods

These neurometabolic signals were quantified using proton magnetic resonance spectroscopy (1H-MRS): N-acetyl aspartate (NAA), creatine-phosphocreatine (Cre), glutamate/glutamine (Glx), myoinositol (mI), and phosphorylcholine-glycerol phosphorylcholine (Cho). Genetic data was collected using the Illumina 1M DuoBeadChip Array from a sample adults with alcohol use disorders (N = 146).

Results

The number of base pairs lost through rare copy number deletions (occurring in less than 5% of our sample) predicted lower NAA, Cre, mI, and Glx. More total rare deletions also predicted lower NAA, Cre, and Glx. Principal components analyses of the five neurometabolites identified two correlated components, the first comprised of NAA, Glx, and Cre, and the second comprised of Cho, mI, and to a lesser extent, Cre. The number and length of rare deletions were correlated with the first component, capturing approximately 10% of phenotypic variance, but not the second component.

Conclusions

These results suggest that mutation load affects neurometabolite concentrations, potentially increasing risk for neuropsychiatric disorders. The greater effect of CVNs on NAA, Glx, and Cre may reflect a greater sensitivity to the effects of mutations, i.e., reduced canalization, for neurometabolites related to metabolic activity and cellular energetics, due to extensive recent selection pressure on these phenotypes in the human lineage.

A 1H magnetic resonance spectroscopic imaging (1H-MRSI) study at 3T and short TE was conducted to evaluate both the reproducibility, as measured by the inter-scan coefficient of variation (CV), and test-retest reliability, as measured by the intraclass correlation coefficient (ICC), of measurements of glutamate (Glu), combined glutamate and glutamine (Glx), myo-inositol (mI), N-acetylaspartate (NAA), creatine, and choline in 21 healthy subjects. The effect of partial volume correction on these measures and the relationship of reproducibility and reliability to data quality were also examined. A 1H-MRSI slice was prescribed above the lateral ventricles and single repeat scans were performed within 30 min to minimize physiologic variability. Inter-scan CVs based on all the voxels varied from 0.05-0.07 for NAA, creatine, and choline to 0.10-0.13 for mI, Glu, and Glx. Findings on the reproducibility of gray and white matter estimates of NAA, creatine, and choline are consistent with previous studies using longer TEs, with CVs in the range of 0.02-0.04 and ICCs in the range of 0.65-0.90. CVs for Glu, Glx, and mI are much lower than reported in previous studies at 1.5T, while white matter mI (CV=0.04, ICC=0.93) and gray matter Glx (CV=0.04, ICC=0.68) demonstrated both high reproducibility and test-retest reliability.

Chronic cocaine use is associated with enhanced cue reactivity to drug stimuli. However, it may also alter functional connectivity (fcMRI) in regions involved in processing drug stimuli. Our aims were to evaluate the neural regions involved in subjective craving and how fcMRI may be altered in chronic cocaine users. Fourteen patients with a confirmed diagnosis of cocaine abuse or dependence (CCA) and 16 gender, age, and education-matched healthy controls (HC) completed a cue reactivity task and a resting state scan while undergoing functional magnetic resonance imaging. CCA showed increased activation compared to HC in left dorsolateral prefrontal and bilateral occipital cortex in response to cocaine cues but not to appetitive control stimuli. Moreover, CCA also showed increased activation within the orbital frontal cortex (OFC) for cocaine cues relative to the appetitive stimuli during a hierarchical regression analysis. A negative association between subjective craving and activity in medial posterior cingulate gyrus (PCC) was also observed for CCA. CCA exhibited increased resting state correlation (positive) between cue-processing seed regions (OFC and ventral striatum), and negative connectivity between cue-processing regions and PCC/precuneus. These alterations in fcMRI may partially explain the neural basis of increased drug cue salience in CCA.

Abstract

Despite the prevalence and impact of mild traumatic brain injury (mTBI), common clinical assessment methods for mTBI have insufficient sensitivity and specificity. Moreover, few researchers have attempted to document underlying changes in physiology as a function of recovery from mTBI. Proton magnetic resonance spectroscopy (1H-MRS) was used to assess neurometabolite concentrations in a supraventricular tissue slab in 30 individuals with semi-acute mTBI, and 30 sex-, age-, and education-matched controls. No significant group differences were evident on traditional measures of attention, memory, working memory, processing speed, and executive skills, though the mTBI group reported significantly more somatic, cognitive, and emotional symptoms. At a mean of 13 days post-injury, white matter concentrations of creatine (Cre) and phosphocreatine (PCre) and the combined glutamate-glutamine signal (Glx) were elevated in the mTBI group, while gray matter concentrations of Glx were reduced. Partial normalization of these three neurometabolites and N-acetyl aspartate occurred in the early days post-injury, during the semi-acute period of recovery. In addition, 17 mTBI patients (57%) returned for a follow-up evaluation (mean = 120 days post-injury). A significant group × time interaction indicated recovery in the mTBI group for gray matter Glx, and trends toward recovery in white matter Cre and Glx. An estimate of premorbid intelligence predicted the magnitude of neurometabolite normalization over the follow-up interval for the mTBI group, indicating that biological factors underlying intelligence may also be associated with more rapid recovery.

Objectives

To identify abnormal function of the limbic cortex (LC) in response to urinary urgency among patients with Overactive Bladder (OAB) using brain functional MRI (fMRI)

Methods

5 OAB subjects and 5 Controls underwent bladder filling and rated urgency sensations while fMRI measured activation in discrete volumes (voxels) within the brain. Changes in brain activation were related to bladder distension and individual subject’s rating of urgency via multiple regression analysis. Beta weights from regression equations were converted into percent signal change (PSC) for each voxel and PSC compared to the null hypothesis using T-tests. Significance threshold of P<.05 was applied along with a cluster size threshold of.32 ml (5 voxels).

Results

OAB patients showed increased brain activation in LC, specifically the insula (IN) and Anterior Cingulate Gyrus (ACG), associated with increased urgency. Urgency sensations during low volumes were associated with bilateral IN activation in OAB subjects (7,621 voxels right IN, 4,453 voxels left IN, mean beta weights .018 +/− .014 and .014 +/− .011) Minimal activation was present in Controls (790 voxels right IN, beta weight =.010 +/− .007). Urgency sensations during high volumes were associated with bilateral ACG activation in OAB subjects (2,304 voxels right IN, 5,005 voxels left IN, mean beta weights of 005 +/− .003 and 004+/−.003) without activation in Controls.

Conclusions

Urinary urgency in patients with OAB is associated with increased activation of the LC. This activation likely represents abnormal processing of sensory input in brain regions associated with emotional response to discomfort.

The semi-acute phase of mild traumatic brain injury (mTBI) is associated with deficits in the cognitive domains of attention, memory, and executive function, which previous work suggests may be related to a specific deficit in disengaging attentional focus. However, to date there have only been a few studies that have employed dynamic imaging techniques to investigate the potential neurological basis of these cognitive deficits during the semi-acute stage of injury. Therefore, event-related functional magnetic resonance imaging (FMRI) was used to investigate the neurological correlates of attentional dysfunction in a clinically homogeneous sample of 16 mTBI patients during the semi-acute phase of injury (< 3 weeks). Behaviorally, mTBI patients exhibited deficits in disengaging and reorienting auditory attention following invalid cues as well as a failure to inhibit attentional allocation to a cued spatial location compared to a group of matched controls. Accordingly, mTBI patients also exhibited hypoactivation within thalamus, striatum, midbrain nuclei and cerebellum across all trials as well as hypoactivation in the right posterior parietal cortex, pre-supplementary motor area, bilateral frontal eye fields and right ventrolateral prefrontal cortex during attentional disengagement. Finally, the hemodynamic response within several regions of the attentional network predicted response times better for controls than mTBI patients. These objective neurological findings represent a potential biomarker for the behavioral deficits in spatial attention that characterize the initial recovery phase of mTBI.

Abstract

Single-voxel proton magnetic resonance imaging (1H-MRS) and proton MR spectroscopic imaging (1H-MRSI) were used to compare brain metabolite levels in semi-acute mild traumatic brain injury (mTBI) patients (n = 10) and matched healthy controls (n = 9). The 1H-MRS voxel was positioned in the splenium, a region known to be susceptible to axonal injury in TBI, and a single 1H-MRSI slice was positioned above the lateral ventricles. To increase sensitivity to the glutamate (Glu) and the combined glutamate-glutamine (Glx) signal, an inter-pulse echo time shown to emphasize the major Glu signals was used along with an analysis method that reduces partial volume errors by using water as a concentration standard. Our preliminary findings indicate significantly lower levels of gray matter Glx and higher levels of white matter creatine-phosphocreatine (Cr) in mTBI subjects relative to healthy controls. Furthermore, Cr levels were predictive of executive function and emotional distress in the combined groups. These results suggest that perturbations in Cr, a critical component of the brain's energy metabolism, and Glu, the brain's major neurotransmitter, may occur following mTBI. Moreover, the different pattern of results for gray and white matter suggests tissue-specific metabolic responses to mTBI.

Previous studies of schizophrenia have suggested a linkage between neuropsychological (NP) deficits and hippocampus abnormality. The relationship between hippocampus volume and NP functioning was investigated in 24 patients with chronic schizophrenia and 24 matched healthy controls. Overall intracranial, white and gray matter, and anterior (AH) and posterior (PH) hippocampus volumes were assessed from magnetic resonance images (MRI). NP domains of IQ, attention, and executive function were also evaluated with respect to volumetric measures. It was hypothesized that AH and PH volumes and episodic memory scores would be positively associated in controls and that the schizophrenia group would depart from this normative pattern. NP functioning was impaired overall and AH volume was smaller in the schizophrenia group. In the controls, the hippocampus–memory relationships involved AH and PH, and correlations were significant for verbal memory measures. In the schizophrenia group, positive correlations were constrained to PH. Negative correlations emerged between AH and verbal and visual memory measures. For both groups, cortical volume negatively correlated with age, but a negative correlation between age and hippocampus volume was found only in the schizophrenia group. In this sample of adults with schizophrenia, atypical relationships between regional hippocampus volumes and episodic memory ability were found, as was an atypical negative association between hippocampus volume and age.

Single-voxel proton magnetic resonance imaging (1H-MRS) and proton MR spectroscopic imaging (1H-MRSI) were used to compare brain metabolite levels in semi-acute mild traumatic brain injury (mTBI) patients (n = 10) and matched healthy controls (n = 9). The 1H-MRS voxel was positioned in the splenium, a region known to be susceptible to axonal injury in TBI, and a single 1H-MRSI slice was positioned above the lateral ventricles. To increase sensitivity to the glutamate (Glu) and the combined glutamate-glutamine (Glx) signal, an inter-pulse echo time shown to emphasize the major Glu signals was used along with an analysis method that reduces partial volume errors by using water as a concentration standard. Our preliminary findings indicate significantly lower levels of gray matter Glx and higher levels of white matter creatine-phosphocreatine (Cr) in mTBI subjects relative to healthy controls. Furthermore, Cr levels were predictive of executive function and emotional distress in the combined groups. These results suggest that perturbations in Cr, a critical component of the brain’s energy metabolism, and Glu, the brain’s major neurotransmitter, may occur following mTBI. Moreover, the different pattern of results for gray and white matter suggests tissue-specific metabolic responses to mTBI.

Changes in the default mode network (DMN) have been linked to multiple neurological disorders including schizophrenia. The anticorrelated relationship the DMN shares with task-related networks permits the quantification of this network both during task (task-induced deactivations: TID) and during periods of passive mental activity (extended rest). However, the effects of different methodologies (TID vs. extended rest) for quantifying the DMN in the same clinical population are currently not well understood. Moreover, several different analytic techniques, including independent component analyses (ICA) and seed-based correlation analyses exist for examining functional connectivity during extended resting states. The current study compared both methodologies and analytic techniques in a group of patients with schizophrenia (SP) and matched healthy controls (HC). Results indicated that TID analyses, ICA, and seed-based correlation all consistently identified the midline (anterior and posterior cingulate gyrus) and lateral parietal cortex as core regions of the DMN, as well as more variable involvement of temporal lobe structures. In addition, SP exhibited increased deactivation during task, as well as decreased functional connectivity with frontal regions and increased connectivity with posterior and subcortical areas during periods of extended rest. The increased posterior and reduced anterior connectivity may partially explain some of the cognitive dysfunction and clinical symptoms that are frequently associated with schizophrenia.

There has been a growing interest in the neuroimaging community regarding resting state data (i.e., passive mental activity) and the subsequent activation of the so-called default mode network (DMN). Although this network was originally characterized by a pattern of deactivation during active cognitive states, more recent applications of data-driven techniques such as independent component analysis (ICA) have permitted the analysis of brain activation during extended periods of truly passive mental activity. However, ICA requires the resultant components to be evaluated for “goodness of fit” via either human raters or more automated techniques. To our knowledge, an investigation on the reliability of either technique in determining the component that best corresponds to default-mode activity has not been performed. Moreover, it is not clear how automated techniques, which are necessarily dependent upon a template mask, are affected by the structures used to compose the mask. The current study investigated both interrater (human-human) reliability and intermethod (human-machine) reliability for determining DMN activation in 42 healthy controls. Results indicated that near perfect interrater reliability was achieved, whereas intermethod reliability was only within the moderate range. The latter was significantly improved via a weighted combination of the anterior and posterior cingulate nodes of the DMN. Implications for fully automating the component selection process are discussed.

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

BACKGROUND

Functional magnetic resonance imaging (FMRI) studies comparing schizophrenia patients and controls may have been confounded by the vascular effects of heavier long-term cigarette use in patients.

METHODS

The blood oxygen level dependent (BOLD) response to a simple sensorimotor task was compared between schizophrenia patient with a smoking history (mean 33 pack years) and carefully matched patient non-smokers and control non-smokers.

RESULTS

Group differences in activation magnitude and spatial extent were non-significant.

CONCLUSIONS

Typical smoking histories in schizophrenia patients do not significantly confound FMRI results in simple sensorimotor tasks when patient demographics are carefully controlled.

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.