Visual hallucinations in schizophrenia investigated with functional magnetic resonance imaging

We investigated a 27-year old patient with paranoid schizophrenia. Brain activity related to visual hallucinations was found in higher visual areas corresponding to the content of the hallucinations (faces, bodies, scenes) and the hippocampus. We assume that the hippocampal activity is related to the retrieval of visual images from memory and that sensory cortex activity is related to the vividness of the perceptual experience.     

Semantic strategy training increases memory performance and brain activity in patients with prefrontal cortex lesions

Objective

Memory deficit is a frequent cognitive disorder following acquired prefrontal cortex lesions. In the present study, we investigated the brain correlates of a short semantic strategy training and memory performance of patients with distinct prefrontal cortex lesions using fMRI and cognitive tests.

Methods

Twenty-one adult patients with post-acute prefrontal cortex (PFC) lesions, twelve with left dorsolateral PFC (LPFC) and nine with bilateral orbitofrontal cortex (BOFC) were assessed before and after a short cognitive semantic training using a verbal memory encoding paradigm during scanning and neuropsychological tests outside the scanner.

Results

After the semantic strategy training both groups of patients showed significant behavioral improvement in verbal memory recall and use of semantic strategies. In the LPFC group, greater activity in left inferior and medial frontal gyrus, precentral gyrus and insula was found after training. For the BOFC group, a greater activation was found in the left parietal cortex, right cingulated and precuneus after training.

Conclusion

The activation of these specific areas in the memory and executive networks following cognitive training was associated to compensatory brain mechanisms and application of the semantic strategy.     

Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis

The Functional Image Analysis Contest (FIAC) 2005 dataset was analyzed using BrainVoyager QX. First, we performed a standard analysis of the functional and anatomical data that includes preprocessing, spatial normalization into Talairach space, hypothesis-driven statistics (one- and two-factorial, single-subject and group-level random effects, General Linear Model [GLM]) of the block- and event-related paradigms. Strong sentence and weak speaker group-level effects were detected in temporal and frontal regions. Following this standard analysis, we performed single-subject and group-level (Talairach-based) Independent Component Analysis (ICA) that highlights the presence of functionally connected clusters in temporal and frontal regions for sentence processing, besides revealing other networks related to auditory stimulation or to the default state of the brain. Finally, we applied a high-resolution cortical alignment method to improve the spatial correspondence across brains and re-run the random effects group GLM as well as the group-level ICA in this space. Using spatially and temporally unsmoothed data, this cortex-based analysis revealed comparable results but with a set of spatially more confined group clusters and more differential group region of interest time courses.     

Independent component model of the default-mode brain function: Assessing the impact of active thinking

The “default-mode” network is an ensemble of cortical regions, which are typically deactivated during demanding cognitive tasks in functional magnetic resonance imaging (fMRI) studies. Using functional connectivity, this network can be conceptualized and studied as a “stand-alone” function or system. Regardless of the task, independent component analysis (ICA) produces a picture of the “default-mode” function even when the subject is performing a simple sensori-motor task or just resting in the scanner. This has boosted the use of default-mode fMRI for non-invasive research in brain disorders. Here, we studied the effect of cognitive load modulation of fMRI responses on the ICA-based pictures of the default-mode function. In a standard graded working memory study based on the n-back task, we used group-level ICA to explore the variability of the default-mode network related to the engagement in the task, in 10 healthy volunteers.

The analysis of the default-mode components highlighted similarities and differences in the layout under three different cognitive loads. We found a load-related general increase of deactivation in the cortical network. Nonetheless, a variable recruitment of the cingulate regions was evident, with greater extension of the anterior and lesser extension of the posterior clusters when switching from lower to higher working memory loads. A co-activation of the hippocampus was only found under no working memory load.

As a generalization of our results, the variability of the default-mode pattern may link the default-mode system as a whole to cognition and may more directly support use of the ICA model for evaluating cognitive decline in brain disorders.     

Source density-driven independent component analysis approach for fMRI data

Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FAST-ICA algorithms employ the underlying assumption that data can be decomposed into statistically independent sources and implicitly model the probability density functions of the underlying sources as highly kurtotic or symmetric. When source data violate these assumptions (e.g., are asymmetric), however, conventional ICA methods might not work well. As a result, modeling of the underlying sources becomes an important issue for ICA applications. We propose a source density-driven ICA (SD-ICA) method. The SD-ICA algorithm involves a two-step procedure. It uses a conventional ICA algorithm to obtain initial independent source estimates for the first-step and then, using a kernel estimator technique, the source density is calculated. A refitted nonlinear function is used for each source at the second step. We show that the proposed SD-ICA algorithm provides flexible source adaptivity and improves ICA performance. On SD-ICA application to fMRI signals, the physiologic meaningful components (e.g., activated regions) of fMRI signals are governed typically by a small percentage of the whole-brain map on a task-related activation. Extra prior information (using a skewed-weighted distribution transformation) is thus additionally applied to the algorithm for the regions of interest of data (e.g., visual activated regions) to emphasize the importance of the tail part of the distribution. Our experimental results show that the source density-driven ICA method can improve performance further by incorporating some a priori information into ICA analysis of fMRI signals.     

Dissociable effects of sleep deprivation on functional connectivity in the dorsal and ventral default mode networks

ObjectiveTo examine changes in functional connectivity of the default mode network (DMN) that are induced by sleep deprivation, and to identify individual differences that contribute to the vulnerability of the brain's response to sleep deprivation.MethodsUsing functional magnetic resonance imaging, we scanned 51 healthy young subjects during the resting state. Of these participants, 28 were scanned following 24 h of sleep deprivation, and 23 age- and education-matched control subjects were scanned after being well rested.ResultsIndependent component analysis was conducted to identify the DMN. Unlike previous studies that consider the DMN as one homogeneous network, the present study found a dissociable effect of sleep deprivation on two subsystems of the DMN. Functional connectivity within the dorsal DMN decreased; this was correlated with longer response times in a psychomotor vigilance task (PVT). An enhanced functional connectivity was found within the ventral DMN as well as between two subsystems, after sleep deprivation. In addition, between-subsystems connectivity was positively correlated with working memory and negatively correlated with the response time of PVT, suggesting a possible compensatory effect of enhanced communication across two subsystems.ConclusionsThe present findings suggest a dissociable effect of sleep deprivation on functional connectivity in the DMN. Lower functional connectivity in dorsal DMN was related to impairments of basic cognitive function. Notably, working memory was positively correlated with the putative compensatory enhanced functional connectivity across two subsystems, which in turn correlated with behavioral performance after sleep deprivation; this suggests that good working memory may play a protective role in sleep deprivation.     

Power spectrum ranked independent component analysis of a periodic fMRI complex motor paradigm

Independent component analysis (ICA) has been demonstrated to be an effective data-driven method for analyzing fMRI data. However, a method for objective differentiation of task-related components from those that are artifactually non-relevant is needed. We propose a method of constant-cycle (periodic) fMRI task paradigm combined with ranking of spatial ICA components by the magnitude contribution of their temporal aspects to the fundamental task frequency. Power spectrum ranking shares some similarity to correlation with an a priori hemodynamic response, but without a need to presume an exact timing or duration of the fMRI response. When applied to a complex motor task paradigm with auditory cues, multiple task-related activations are successfully identified and separated from artifactual components. These activations include sensorimotor, auditory, and superior parietal areas. Comparisons of task-related component time courses indicate the temporal relationship of fMRI responses in functionally involved regions. Results indicate the sensitivity of ICA to short-duration hemodynamics, and the efficacy of a power spectrum ranking method for identification of task-related components.     

Isolation of epileptiform discharges from unaveraged EEG by independent component analysis.

OBJECTIVE: We propose a method that allows the separation of epileptiform discharges (EDs) from the EEG background, including the ED's waveform and spatial distribution. The method even allows to separate a spike in two components occurring at approximately the same time but having different waveforms and spatial distributions. METHODS: The separation employs independent component analysis (ICA) and is not based on any assumption regarding generator model. A simulation study was performed by generating ten EEG data matrices by computer: each matrix included real background activity from a normal subject to which was added an array of simulated unaveraged EDs. Each discharge was a summation of two transients having slightly different potential field distributions and small jitters in time and amplitude. Real EEG data were also obtained from three epileptic patients. RESULTS: Through ICA, we could isolate the two epileptiform transients in every simulation matrix, and the retrieved transients were almost identical as the originals, especially in their spatial distributions. Two epileptic components were isolated by ICA in all patients. Each estimated epileptic component had a consistent time course. CONCLUSION: ICA appears promising for the separation of unaveraged spikes from the EEG background and their decomposition in independent spatio-temporal components.     

Implications of cortical balanced excitation and inhibition,functional heterogeneity,and sparseness of neuronal activity in fMRI

Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies often report inconsistent findings, probably due to brain properties such as balanced excitation and inhibition and functional heterogeneity. These properties indicate that different neurons in the same voxels may show variable activities including concurrent activation and deactivation, that the relationships between BOLD signal and neural activity (i.e., neurovascular coupling) are complex, and that increased BOLD signal may reflect reduced deactivation, increased activation, or both. The traditional general-linear-model-based-analysis (GLM-BA) is a univariate approach, cannot separate different components of BOLD signal mixtures from the same voxels, and may contribute to inconsistent findings of fMRI. Spatial independent component analysis (sICA) is a multivariate approach, can separate the BOLD signal mixture from each voxel into different source signals and measure each separately, and thus may reconcile previous conflicting findings generated by GLM-BA. We propose that methods capable of separating mixed signals such as sICA should be regularly used for more accurately and completely extracting information embedded in fMRI datasets.     

Functional brain network abnormalities during verbal working memory performance in adolescents and young adults with dyslexia

Behavioral and functional neuroimaging studies indicate deficits in verbal working memory (WM) and frontoparietal dysfunction in individuals with dyslexia. Additionally, structural brain abnormalities in dyslexics suggest a dysconnectivity of brain regions associated with phonological processing. However, little is known about the functional neuroanatomy underlying cognitive dysfunction in dyslexia. In this study, functional magnetic resonance imaging and multivariate analytic techniques were used to investigate patterns of functional connectivity during a verbal WM task in individuals with dyslexia (n = 12) and control subjects (n = 13). Dyslexics were not significantly slower than controls; however, they were less accurate with increasing WM demand. Independent component analysis identified 18 independent components (ICs) among which two ICs were selected for further analyses. These ICs included functional networks which were positively correlated with the delay period of the activation task in both healthy controls and dyslexics. Connectivity abnormalities in dyslexics were detected within both networks of interest: within a “phonological” left-lateralized prefrontal network, increased functional connectivity was found in left prefrontal and inferior parietal regions. Within an “executive” bilateral frontoparietal network, dyslexics showed a decreased connectivity pattern comprising bilateral dorsolateral prefrontal and posterior parietal regions, while increased connectivity was found in the left angular gyrus, the left hippocampal cortex and the right thalamus. The functional connectivity strength in the latter regions was associated with WM task accuracy and with the numbers of errors during a spelling test. These data suggest functional connectivity abnormalities in two spatiotemporally dissociable brain networks underlying WM dysfunction in individuals with dyslexia.     

Children with reading disorder show modality independent brain abnormalities during semantic tasks

Neuroimaging studies have suggested that left inferior frontal gyrus, left inferior parietal lobule and left middle temporal gyrus are critical for semantic processing in normal children. The goal of the present functional magnetic resonance imaging (fMRI) study was to determine whether these regions are systematically related to semantic processing in children (9- to 15-year-old) diagnosed with reading disorders (RD). Semantic judgments required participants to indicate whether two words were related in meaning. The strength of semantic association varied continuously from higher association pairs (e.g., king-queen) to lower association pairs (e.g. net-ship). We found that the correlation between association strength and activation was significantly weaker for RD children compared to controls in left middle temporal gyrus and left inferior parietal lobule for both the auditory and the visual modalities and in left inferior frontal gyrus for the visual modality. These results suggest that the RD children have abnormalities in semantic search/retrieval in the inferior frontal gyrus, integration of semantic information in the inferior parietal lobule and semantic lexical representations in the middle temporal gyrus. These deficits appear to be general to the semantic system and independent of modality.     

弓状束纤维示踪与功能磁共振成像定位额叶语言皮质的比较研究

目的 联合弓状束纤维示踪技术和任务态fMRI,评价弓状束终末投影定位额叶语言皮质的临床可行性.方法 采用任务态fMRI定位语言皮质激活区,并纤维示踪定位弓状束终末端;将两者皮质投影在导航系统融合,比较两者吻合度.结果 弓状束终末投影区域与任务态fMRI语言皮质激活区高度吻合.弓状束投影区主要位于左侧中央前回腹侧部(87.5％)及左侧额下回(75.0％).弓状束终末投影平均半径(R1)=(12.4±5.3)mm;fMRl语言任务激活区平均半径(R2)=(10.9±4.6) mm;两者中心距离(D)=(10.6±6.9)mm.结论 弓状束纤维示踪不仅可用于定位和保护皮质下语言通路,还可用于额叶语言皮质定位,并与fMRI的定位结果吻合.     

Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest

Cortical functional connectivity, as indicated by the concurrent spontaneous activity of spatially segregated regions, is being studied increasingly because it may determine the reaction of the brain to external stimuli and task requirements and it is reportedly altered in many neurological and psychiatric disorders. In functional magnetic resonance imaging (fMRI), such functional connectivity is investigated commonly by correlating the time course of a chosen "seed voxel" with the remaining voxel time courses in a voxel-by-voxel manner. This approach is biased by the actual choice of the seed voxel, however, because it only shows functional connectivity for the chosen brain region while ignoring other potentially interesting patterns of coactivation. We used spatial independent component analysis (sICA) to assess cortical functional connectivity maps from resting state data. SICA does not depend on any chosen temporal profile of local brain activity. We hypothesized that sICA would be able to find functionally connected brain regions within sensory and motor regions in the absence of task-related brain activity. We also investigated functional connectivity patterns of several parietal regions including the superior parietal cortex and the posterior cingulate gyrus. The components of interest were selected in an automated fashion using predefined anatomical volumes of interest. SICA yielded connectivity maps of bilateral auditory, motor and visual cortices. Moreover, it showed that prefrontal and parietal areas are also functionally connected within and between hemispheres during the resting state. These connectivity maps showed an extremely high degree of consistency in spatial, temporal, and frequency parameters within and between subjects. These results are discussed in the context of the recent debate on the functional relevance of fluctuations of neural activity in the resting state.     

Functional MRI characteristics of a focal region of cortical malformation not associated with seizure onset

Neuroimaging studies have demonstrated that heterotopic tissue of patients with "double cortex" is activated during motor and somatosensory tasks. Activation in patients with malformations of cortical development (MCD) has been variable, likely due to the heterogeneity of the disorder. We examined clinical, electroencephalography (EEG), neuropsychological, and functional MRI findings in a patient with intractable epilepsy secondary to MCD in the left temporal cortex. Invasive EEG monitoring revealed that the dysplastic tissue was not involved in ictal onset of seizures. Functional MRI tests of motion and object processing, memory encoding, and language demonstrated no activation within dysplastic tissue. Hemispheric asymmetries in activation for motion and object processing were evident, favoring the right hemisphere--a pattern not evident in controls. These weaker activations in the patient were present in tissue proximal to the seizure focus. Thus, nonepileptogenic dysplastic tissue may not support cognitive functions, with abnormal processing evident in epileptogenic tissue.     

Differential learning algorithms for decorrelation and independent component analysis

Decorrelation and its higher-order generalization, independent component analysis (ICA), are fundamental and important tasks in unsupervised learning, that were studied mainly in the domain of Hebbian learning. In this paper we present a variation of the natural gradient ICA, differential ICA, where the learning relies on the concurrent change of output variables. We interpret the differential learning as the maximum likelihood estimation of parameters with latent variables represented by the random walk model. In such a framework, we derive the differential ICA algorithm and, in addition, we also present the differential decorrelation algorithm that is treated as a special instance of the differential ICA. Algorithm derivation and local stability analysis are given with some numerical experimental results.     

TCA-fMRI技术在难治性癫痫致痫灶术前定位中的应用

目的探讨静息态功能性磁共振时间簇分析（TCA-fMRI）应用于难治性癫痫致痫灶术前定位的方法和价值。方法对11例难治性癫痫病人,在癫痫发作间期行静息态功能磁共振（fMRI）检查,应用TCA-fMRI技术计算与癫痫发作相关的脑内激活区（致区）,并分析比较该激活区与术中皮质电极定位致痫灶之间的吻合程度。结果 TCA-fMRI确定的激活区与术中皮质电极定位的致痫灶一致6例;激活区范围扩大3例,但最强激活区仍与皮质电极定位的癫痫灶一致;激活区较弥散2例。术后病理显示：胶质瘤3例,脑软化灶4例,灰质异位症2例,海绵状血管瘤1例,海马萎缩1例。随访1年,术后3个月、6个月及1年均复查脑电图,癫痫发作消失8例,明显减少3例;无严重并发症。结论静息态TCA-fMRI技术是一种新型、无创性的致痫灶定位方法,将静息状态下致痫灶异常放电所导致的血氧依赖水平变化在MRI三维结构图像上显示,能在术前精确定位致痫灶。     

Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming

This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.     

A Bayesian inverse solution using independent component analysis

We present new results about the simultaneous linear inverse problems using independent component analysis (ICA), which can be used to separate the data into statistically independent components. The idea of using ICA in solving such inverse problems, especially in EEG/MEG context, has been a known topic for at least more than a decade, but the known results have been justified heuristically, and their relationships are not understood properly. Here we show how to obtain a Bayesian posterior for a spatial source distribution, by using an ICA demixing matrix as an input. The posterior enables us to rederive and reinterpret the previously known methods, and also provides completely new methods.