Person:

Kubicki, Marek

A fundamental tenet of the “disconnectivity” theories of schizophrenia is that the disorder is ultimately caused by abnormal communication between spatially disparate brain structures. Given that the white matter fasciculi represent the primary infrastructure for long distance communication in the brain, abnormalities in these fiber bundles have been implicated in the etiology of schizophrenia. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the visualization of white matter macrostructure in vivo, and which has provided unprecedented insight into the existence and nature of white matter abnormalities in schizophrenia. The paper begins with an overview of DTI and more commonly used diffusion metrics and moves on to a brief review of the schizophrenia literature. The functional implications of white matter abnormalities are considered, particularly with respect to myelin's role in modulating the transmission velocity of neural discharges. The paper concludes with a speculative hypothesis about the relationship between gray and white matter abnormalities associated with schizophrenia.

Background—Abnormalities within language-related anatomical structures have been associated with clinical symptoms and with language and memory deficits in schizophrenia. Recent studies suggest disruptions in functional connectivity within the Inferior Frontal Gyrus (IFG) network in schizophrenia. However, due to technical challenges, anatomical connectivity abnormalities within this network and their involvement in clinical and cognitive deficits have not been studied. Material and Methods—Diffusion and anatomical scans were obtained from 23 chronic schizophrenia patients and 23 matched controls. The IFG was automatically segmented, and its white matter connections extracted and measured with newly-developed stochastic tractography tools. Correlations between anatomical structures and measures of semantic processing were also performed. Results—White Matter connections between the IFG and posterior brain regions followed two distinct pathways: dorsal and ventral. Both demonstrated left lateralization, but ventral pathway abnormalities were only found in schizophrenia. IFG volumes also showed left lateralization andabnormalities in schizophrenia. Further, despite similar laterality and abnormality patterns, IFG volumes and white matter connectivity were not correlated with each other in either group. Interestingly, measures of semantic processing correlated with white matter connectivity in schizophrenia and with gray matter volumes in controls. Finally, hallucinations were best predicted by both gray matter and white matter measures together. Conclusions—Our results suggest abnormalities within the ventral IFG network in schizophrenia, with white matter abnormalities better predicting semantic deficits. The lack of a statistical relationship between coexisting gray and white matter deficits might suggest their different origin and the necessity for a multimodal approach in future schizophrenia studies.

Background—Several theories of schizophrenia have emphasized the role of aberrant neural timing in the etiology of the disease, possibly as a consequence of conduction delays caused by structural damage to the white-matter fasciculi. Consistent with this theory, increased interhemispheric transmission times (IHTTs) to unilaterally-presented visual stimuli have been reported in patients with schizophrenia. The present study investigated whether or not these IHTT abnormalities could be underpinned by structural damage to the visual fibers of the corpus callosum. Methods—30 schizophrenia patients and 22 matched controls underwent Event Related Potential (ERP) recording, and a subset of 19 patients and 16 controls also underwent 3T Diffusion-Tensor Imaging (DTI). Unilateral visual stimuli (squares, 2 × 2 degrees) were presented 6 degrees lateral to either side of a central fixation point. IHTTs (ipsilateral minus contralateral latencies) were calculated for the P1 and N1 components at occipital-temporal sites in current source densitytransformed ERPs. The visual fibers of the corpus callosum were extracted with streamline tractography and the diffusion metrics of Fractional Anisotropy (FA) and Mode calculated. Results—While both subject groups exhibited highly significant IHTTs across a range of posterior electrode pairs, and significantly shorter IHTTs from left-to-right hemisphere than vice versa, no significant groupwise differences in IHTT were observed. However, participants’ IHTTs were linearly related to their FA and Mode, with longer IHTTs being associated with lower FA and more prolate diffusion ellipsoids. Conclusions—These results suggest that IHTTs are estimable from DTI measures of white matter integrity. In light of the range of diffusion abnormalities that have been reported in patients with schizophrenia, particularly in frontal fasciculi, these results support the conjecture that schizophrenia is ultimately underpinned by abnormalities in neural timing.

We introduce a method for combining fiber tracking from diffusion-tensor (DT) imaging with cortical gray matter parcellation from structural high-spatial-resolution 3D spoiled gradient-recalled acquisition in the steady state images. We applied this method to a tumor case to determine the impact of the tumor on white matter architecture. We conclude that this new method for combining structural and DT imaging data is useful for understanding cortical connectivity and the localization of fiber tracts and their relationship with cortical anatomy and brain abnormalities.

Voxel-based morphometry (VBM) may afford a more rapid and extensive survey of gray matter abnormalities in schizophrenia than manually drawn region of interest (ROI) analysis, the current gold standard in structural MRI. Unfortunately, VBM has not been validated by comparison with ROI analyses, nor used in first-episode patients with schizophrenia or affective psychosis, who lack structural changes associated with chronicity. An SPM99-based implementation of VBM was used to compare a group of 16 patients with first-episode schizophrenia and a group of 18 normal controls and, as a further comparison, 16 first-episode patients with affective psychosis. All groups were matched for age and handedness. High spatial resolution structural images were normalized to the SPM99 template and then segmented, smoothed, and subjected to an ANCOVA. Schizophrenia vs control group comparisons: Voxel-by-voxel comparison of gray matter densities showed that only the left STG region was significantly different when corrected for multiple comparisons P < .05), consistent with our previously reported manual ROI results. Analysis of the extent of voxel clusters, replicated with permutation analyses, revealed group differences in bilateral anterior cingulate gyri and insula (not previously examined by us with manually drawn ROI) and unilateral parietal lobe, but not in medial temporal lobe (where our ROI analysis had shown differences). However, use of a smaller smoothing kernel and a small volume correction revealed left-sided hippocampal group differences. Affective psychosis comparisons: When the same statistical thresholding criteria were used, no significant differences between affective psychosis patients and controls were noted. Since a major interest was whether patients with affective psychosis shared some anatomical abnormalities with schizophrenia, we applied a small volume correction and searched within the regions that were significantly less dense in schizophrenia compared to control subjects. With this statistical correction, the insula showed, bilaterally, the same pattern of differences in affective disorder subjects as that in schizophrenic subjects, whereas both left STG and left hippocampus showed statistical differences between affectives and schizophrenics, indicating the abnormalities specific to first-episode schizophrenia. These findings suggest both the promise and utility of VBM in evaluating gray matter abnormalities. They further suggest the importance of comparing VBM findings with more traditional ROI analyses until the reasons for the differences between methods are determined.

Objective: Disruptions in connectivity between the frontal and temporal lobes may explain some of the symptoms observed in schizophrenia. Conventional magnetic resonance imaging (MRI) studies, however, have not shown compelling evidence for white matter abnormalities, because white matter fiber tracts cannot be visualized by conventional MRI. Diffusion tensor imaging is a relatively new technique that can detect subtle white matter abnormalities in vivo by assessing the degree to which directionally organized fibers have lost their normal integrity. The first three diffusion tensor imaging studies in schizophrenia showed lower anisotropic diffusion, relative to comparison subjects, in whole-brain white matter, prefrontal and temporal white matter, and the corpus callosum, respectively. Here the authors focus on fiber tracts forming temporal-frontal connections. Method: Anisotropic diffusion was assessed in the uncinate fasciculus, the most prominent white matter tract connecting temporal and frontal brain regions, in 15 patients with chronic schizophrenia and 18 normal comparison subjects. A 1.5-T GE Echospeed system was used to acquire 4-mm-thick coronal line-scan diffusion tensor images. Maps of the fractional anisotropy were generated to quantify the water diffusion within the uncinate fasciculus. Results: Findings revealed a group-by-side interaction for fractional anisotropy and for uncinate fasciculus area, derived from automatic segmentation. The patients with schizophrenia showed a lack of normal left-greater-than-right asymmetry seen in the comparison subjects. Conclusions: These findings demonstrate the importance of investigating white matter tracts in vivo in schizophrenia and support the hypothesis of a disruption in the normal pattern of connectivity between temporal and frontal brain regions in schizophrenia.

The medial orbitofrontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) are part of a wider neural network that plays an important role in general intelligence and executive function. We used structural brain imaging to quantify magnetic resonance gray matter volume and diffusion tensor white matter integrity of the mOFC-rACC network in 26 healthy participants who also completed neuropsychological tests of intellectual abilities and executive function. Stochastic tractography, the most effective Diffusion Tensor Imaging method for examining white matter connections between adjacent gray matter regions, was employed to assess the integrity of mOFC-rACC pathways. Fractional anisotropy (FA), which reflects the integrity of white matter connections, was calculated. Results indicated that higher intelligence correlated with greater gray matter volumes for both mOFC and rACC, as well as with increased FA for left posterior mOFC-rACC connectivity. Hierarchical regression analyses revealed that DTI-derived FA of left posterior mOFC-rACC uniquely accounted for 29%–34% of the variance in IQ, in comparison to 11%–16% uniquely explained by gray matter volume of the left rACC. Together, left rACC gray matter volume and white matter connectivity between left posterior mOFC and rACC accounted for up to 50% of the variance in general intelligence. This study is to our knowledge the first to examine white matter connectivity between OFC and ACC, two gray matter regions of interests that are very close in physical proximity, and underscores the important independent contributions of variations in rACC gray matter volume and mOFC-rACC white matter connectivity to individual differences in general intelligence.

The main contribution of this work is the careful syntactical definition of major white matter tracts in the human brain based on a neuroanatomist’s expert knowledge. We present a technique to formally describe white matter tracts and to automatically extract them from diffusion MRI data. The framework is based on a novel query language with a near-to-English textual syntax. This query language allows us to construct a dictionary of anatomical definitions describing white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This enables automated coherent labeling of white matter anatomy across subjects. We use our method to encode anatomical knowledge in human white matter describing 10 association and 8 projection tracts per hemisphere and 7 commissural tracts. The technique is shown to be comparable in accuracy to manual labeling. We present results applying this framework to create a white matter atlas from 77 healthy subjects, and we use this atlas in a proof-of-concept study to detect tract changes specific to schizophrenia.

We investigated the association between the Val158Met polymorphism of the catechol-O-methyltransferase (COMT) gene, the Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene, and white matter changes in patients with major depressive disorder (MDD) and healthy subjects using diffusion tensor imaging (DTI). We studied 30 patients with MDD (17 males and 13 females, with mean age ± standard deviation [SD] =44±12 years) and 30 sex- and age-matched healthy controls (17 males and 13 females, aged 44±13 years). Using DTI analysis with a tract-based spatial statistics (TBSS) approach, we investigated the differences in fractional anisotropy, radial diffusivity, and axial diffusivity distribution among the three groups (patients with the COMT gene Val158Met, those with the BDNF gene Val66Met, and the healthy subjects). In a voxel-wise-based group comparison, we found significant decreases in fractional anisotropy and axial diffusivity within the temporal lobe white matter in the Met-carriers with MDD compared with the controls (P<0.05). No correlations in fractional anisotropy, axial diffusivity, or radial diffusivity were observed between the MDD patients and the controls, either among those with the BDNF Val/Val genotype or among the BDNF Met-carriers. These results suggest an association between the COMT gene Val158Met and the white matter abnormalities found in the temporal lobe of patients with MDD.

The cingulum bundle (CB) connects gray matter structures of the limbic system and as such has been implicated in the etiology of schizophrenia. There is growing evidence to suggest that the CB is actually comprised of a conglomeration of discrete sub-connections. The present study aimed to use Diffusion Tensor tractography to subdivide the CB into its constituent sub-connections, and to investigate the structural integrity of these sub-connections in patients with schizophrenia and matched healthy controls. Diffusion Tensor Imaging scans were acquired from 24 patients diagnosed with chronic schizophrenia and 26 matched healthy controls. Deterministic tractography was used in conjunction with FreeSurfer-based regions-of-interest to subdivide the CB into 5 sub-connections (I1 to I5). The patients with schizophrenia exhibited subnormal levels of FA in two cingulum sub-connections, specifically the fibers connecting the rostral and caudal anterior cingulate gyrus (I1) and the fibers connecting the isthmus of the cingulate with the parahippocampal cortex (I4). Furthermore, while FA in the I1 sub-connection was correlated with the severity of patients' positive symptoms (specifically hallucinations and delusions), FA in the I4 sub-connection was correlated with the severity of patients' negative symptoms (specifically affective flattening and anhedonia/asociality). These results support the notion that the CB is a conglomeration of structurally interconnected yet functionally distinct sub-connections, of which only a subset are abnormal in patients with schizophrenia. Furthermore, while acknowledging the fact that the present study only investigated the CB, these results suggest that the positive and negative symptoms of schizophrenia may have distinct neurobiological underpinnings.