Identification of degenerate neuronal systems based on intersubject variability

Narrative analysis in individuals with Parkinson's disease following intensive voice treatment: secondary outcome variables from a randomized controlled trial

Communication is often impaired in individuals with Parkinson's disease (PD), typically secondary to sensorimotor deficits impacting voice and speech. Language may also be diminished in PD, particularly for production and comprehension of verbs. Evidence exists that verb processing is influenced by motor system modulation suggesting that verb deficits in PD are underpinned by similarities in the neural representations of actions that span motor and semantic systems. Conversely, subtle differences in cognition in PD may explain difficulty in processing of complex syntactic forms, which increases cognitive demand and is linked to verb use. Here we investigated whether optimizing motor system support for vocal function (improving loudness) affects change in lexical semantic, syntactic, or informativeness aspects of spoken discourse. Picture description narratives were compared for 20 Control participants and 39 with PD, 19 of whom underwent Lee Silverman Voice Treatment (LSVT LOUD®). Treated PD narratives were also contrasted with those of untreated PD and Control participants at Baseline and after treatment. Controls differed significantly from the 39 PD participants for verbs per utterance, but this difference was largely driven by untreated PD participants who produced few utterances but with verbs, inflating their verbs per utterance. Given intervention, there was a significant increase in vocal loudness but no significant changes in language performance. These data do not support the hypothesis that targeting this speech motor system results in improved language production. Instead, the data provide evidence of considerable variability in measures of language production across groups, particularly in verbs per utterance.

Context-sensitive computational mechanistic explanation in cognitive neuroscience

Mainstream cognitive neuroscience aims to build mechanistic explanations of behavior by mapping abilities described at the organismal level via the subpersonal level of computation onto specific brain networks. We provide an integrative review of these commitments and their mismatch with empirical research findings. Context-dependent neural tuning, neural reuse, degeneracy, plasticity, functional recovery, and the neural correlates of enculturated skills each show that there is a lack of stable mappings between organismal, computational, and neural levels of analysis. We furthermore highlight recent research suggesting that task context at the organismal level determines the dynamic parcellation of functional components at the neural level. Such instability prevents the establishment of specific computational descriptions of neural function, which remains a central goal of many brain mappers - including those who are sympathetic to the notion of many-to-many mappings between organismal and neural levels. This between-level instability presents a deep epistemological challenge and requires a reorientation of methodological and theoretical commitments within cognitive neuroscience. We demonstrate the need for change to brain mapping efforts in the face of instability if cognitive neuroscience is to maintain its central goal of constructing computational mechanistic explanations of behavior; we show that such explanations must be contextual at all levels.

Correlation between brain function and ADHD symptom changes in children with ADHD following a few-foods diet: an open-label intervention trial

Research into the effect of nutrition on attention-deficit hyperactivity disorder (ADHD) in children has shown that the few-foods diet (FFD) substantially decreases ADHD symptoms in 60% of children. However, the underlying mechanism is unknown. In this open-label nutritional intervention study we investigated whether behavioural changes after following an FFD are associated with changes in brain function during inhibitory control in 79 boys with ADHD, aged 8–10 years. Parents completed the ADHD Rating Scale before (t1) and after the FFD (t2). Functional magnetic resonance imaging (fMRI) scans were acquired during a stop-signal task at t1 and t2, and initial subject-level analyses were done blinded for ARS scores. Fifty (63%) participants were diet responders, showing a decrease of ADHD symptoms of at least 40%. Fifty-three children had fMRI scans of sufficient quality for further analysis. Region-of-interest analyses demonstrated that brain activation in regions implicated in the stop-signal task was not associated with ADHD symptom change. However, whole-brain analyses revealed a correlation between ADHD symptom decrease and increased precuneus activation (p_FWE(cluster) = 0.015 for StopSuccess > Go trials and p_FWE(cluster) < 0.001 for StopSuccess > StopFail trials). These results provide evidence for a neurocognitive mechanism underlying the efficacy of a few-foods diet in children with ADHD.

Degeneracy and Complexity in Neuro-Behavioral Correlates of Team Coordination

Team coordination-members of a group acting together rather than performing specific actions individually-is essential for success in many real-world tasks such as military missions, sports, workplace, or school interactions. However, team coordination is highly variable, which is one reason why its underlying neural processes are largely unknown. Here we used dual electroencephalography (EEG) in dyads to study the neurobehavioral dynamics of team coordination in an ecologically valid task that places intensive demands on joint performance. We present a novel conceptual framework to interpret neurobehavioral variability in terms of degeneracy, a fundamental property of complex biological systems said to enhance flexibility and robustness. We characterize degeneracy conceptually in terms of a manifold representing the geometric locus of the dynamics in the high dimensional state-space of neurobehavioral signals. The geometry and dimensionality of the manifold are determined by task constraints and team coordination requirements which restrict the manifold to trajectories that are conducive to successful task performance. Our results indicate that team coordination is associated with dimensionality reduction of the manifold as evident in increased inter-brain phase coherence of beta and gamma rhythms during critical phases of task performance where subjects exchange information. Team coordination was also found to affect the shape of the manifold manifested as a symmetry breaking of centro-parietal wavelet power patterns across subjects in trials with high team coordination. These results open a conceptual and empirical path to identifying the mechanisms underlying team performance in complex tasks.

Disentangling subgroups of participants recruiting shared as well as different brain regions for the execution of the verb generation task: A data-driven fMRI study

The spatial pattern of task-related brain activity in fMRI studies might be expected to change according to several variables such as handedness and age. However this spatial heterogeneity might also be due to other unmodeled sources of inter-subject variability. Since group-level results reflect patterns of task-evoked brain activity common to most of the subjects in the sample, they could conceal the presence of subgroups recruiting other brain regions beyond the common pattern. To deal with these issues, data-driven methods can be used to detect the presence of sources of inter-subject variability that might be hard to identify and therefore model a priori. Here we assess the potential of Independent Component Analysis (ICA) to detect the presence of unexpected subgroups of participants. To this end, we acquired task-evoked fMRI data on 45 healthy adults using the verb generation (VGEN) task, in which participants are visually presented with the noun of an object of everyday use, and asked to covertly generate a verb describing the corresponding action. As expected, the task elicited activity in a temporo-parieto-frontal network typically found in previous VGEN experiments. We then quantified the contribution of every subject to nine task-related spatio-temporal processes identified by ICA. A cluster analysis of this quantity yielded three subgroups of participants. Differences between the three identified subgroups were distributed in left and right prefrontal, posterior parietal and extrastriate occipital regions. These results could not be explained by differences in sex, age or handedness across the participants. Furthermore, some regions where a significant difference was found between subgroups were not present in the group-level pattern of task-related activity. We discuss the potential application of this approach for characterizing brain activity in different subgroups of patients with neuropsychiatric or neurological conditions.

Inherent Structure-Based Multiview Learning With Multitemplate Feature Representation for Alzheimer's Disease Diagnosis

Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods.

Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment

As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.

Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis

Recently, neuroimaging-based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis has attracted researchers in the field, due to the increasing prevalence of the diseases. Unfortunately, the unfavorable high-dimensional nature of neuroimaging data, but a limited small number of samples available, makes it challenging to build a robust computer-aided diagnosis system. Machine learning techniques have been considered as a useful tool in this respect and, among various methods, sparse regression has shown its validity in the literature. However, to our best knowledge, the existing sparse regression methods mostly try to select features based on the optimal regression coefficients in one step. We argue that since the training feature vectors are composed of both informative and uninformative or less informative features, the resulting optimal regression coefficients are inevidently affected by the uninformative or less informative features. To this end, we first propose a novel deep architecture to recursively discard uninformative features by performing sparse multi-task learning in a hierarchical fashion. We further hypothesize that the optimal regression coefficients reflect the relative importance of features in representing the target response variables. In this regard, we use the optimal regression coefficients learned in one hierarchy as feature weighting factors in the following hierarchy, and formulate a weighted sparse multi-task learning method. Lastly, we also take into account the distributional characteristics of samples per class and use clustering-induced subclass label vectors as target response values in our sparse regression model. In our experiments on the ADNI cohort, we performed both binary and multi-class classification tasks in AD/MCI diagnosis and showed the superiority of the proposed method by comparing with the state-of-the-art methods.

Clustering-induced multi-task learning for AD/MCI classification

In this work, we formulate a clustering-induced multi-task learning method for feature selection in Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak distribution of classes. The rationale for our approach is that it is likely for neuroimaging data to have multiple peaks or modes in distribution due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak. We then encode the respective subclasses, i.e., clusters, with their unique codes by imposing the subclasses of the same original class close to each other and those of different original classes L2,1-penalized regression framework by taking the codes as new label vectors of our training samples, through which we select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by achieving the maximal classification accuracies of 95.18% (AD/Normal Control: NC), 79.52% (MCI/NC), and 72.02% (MCI converter/MCl non-converter), outperforming the competing single-task learning method.

Subclass-based multi-task learning for Alzheimer's disease diagnosis

In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74.04% (MCI-C vs. MCI-NC), respectively.

Different neural capacity limitations for articulatory and non-articulatory maintenance of verbal information

Many studies have demonstrated attenuated verbal working memory (WM) under articulatory suppression. However, performance is not completely abolished, suggesting a less efficient, non-articulatory mechanism for the maintenance of verbal information. The neural causes for the reduced efficiency of such a putative complementary maintenance system have not yet been addressed. The present study was conducted to fill this gap. Subjects performed a Sternberg task (a) under articulatory maintenance at low, high, and supracapacity set sizes and (b) under non-articulatory maintenance at low and high set sizes. With functional magnetic resonance imaging, set-size related increases in activity were compared between subvocal articulatory rehearsal and non-articulatory maintenance. First, the results replicate previous findings showing different networks underlying these two maintenance strategies. Second, activation of all key nodes of the articulatory maintenance network increased with the amount of memorized information, showing no plateau at high set sizes. In contrast, for non-articulatory maintenance, there was evidence for a plateau at high set sizes in all relevant areas of the network. Third, for articulatory maintenance, the non-articulatory maintenance network was additionally recruited at supracapacity set sizes, presumably to assist processing in this highly demanding condition. This is the first demonstration of differential neural bottlenecks for articulatory and non-articulatory maintenance. This study adds to our understanding of the performance differences between these two strategies supporting verbal WM.

Changes in auditory feedback connections determine the severity of speech processing deficits after stroke

We compared brain structure and function in two subgroups of 21 stroke patients with either moderate or severe chronic speech comprehension impairment. Both groups had damage to the supratemporal plane; however, the severe group suffered greater damage to two unimodal auditory areas: primary auditory cortex and the planum temporale. The effects of this damage were investigated using fMRI while patients listened to speech and speech-like sounds. Pronounced changes in connectivity were found in both groups in undamaged parts of the auditory hierarchy. Compared to controls, moderate patients had significantly stronger feedback connections from planum temporale to primary auditory cortex bilaterally, while in severe patients this connection was significantly weaker in the undamaged right hemisphere. This suggests that predictive feedback mechanisms compensate in moderately affected patients but not in severely affected patients. The key pathomechanism in humans with persistent speech comprehension impairments may be impaired feedback connectivity to unimodal auditory areas.

Neural networks engaged in tactile object manipulation: patterns of expression among healthy individuals

BACKGROUND

Somatosensory object discrimination has been shown to involve widespread cortical and subcortical structures in both cerebral hemispheres. In this study we aimed to identify the networks involved in tactile object manipulation by principal component analysis (PCA) of individual subjects. We expected to find more than one network.

METHODS

Seven healthy right-handed male volunteers (aged 22 to 44 yrs) manipulated with their right hand aluminium spheres during 5 s with a repetition frequency of 0.5-0.7 Hz. The correlation coefficients between the principal component temporal expression coefficients and the hemodynamic response modelled by SPM (ecc) determined the task-related components. To establish reproducibility within subjects and similarity of functional connectivity patterns among subjects, regional correlation coefficients (rcc) were computed between task-related component image volumes. By hierarchically categorizing, selecting and averaging the task-related component image volumes across subjects according to the rccs, mean component images (MCIs) were derived describing neural networks associated with tactile object manipulation.

RESULTS

Two independent mean component images emerged. Each included the primary sensorimotor cortex contralateral to the manipulating hand. The region extended to the premotor cortex in MCI 1, whereas it was restricted to the hand area of the primary sensorimotor cortex in MCI 2. MCI 1 showed bilateral involvement of the paralimbic anterior cingulate cortex (ACC), whereas MCI 2 implicated the midline thalamic nuclei and two areas of the rostral dorsal pons.

CONCLUSIONS

Two distinct networks participate in tactile object manipulation as revealed by the intra- and interindividual comparison of individual scans. Both were employed by most subjects, suggesting that both are involved in normal somatosensory object discrimination.

The main sources of intersubject variability in neuronal activation for reading aloud

The aim of this study was to find the most prominent source of intersubject variability in neuronal activation for reading familiar words aloud. To this end, we collected functional imaging data from a large sample of subjects (n = 76) with different demographic characteristics such as handedness, sex, and age, while reading. The subject-by-subject error variance was estimated from a one-sample t test (on all 76 subjects) and was reduced to a lower dimension using principal components decomposition. A Gaussian Mixture Model was then applied to dissociate different subgroups of subjects that explained the main sources of variability in the data. This resulted in the identification of four different subject groups. The comparison of these subgroups to the subjects' demographic details showed that age had a significant effect on the subject partitioning. In addition, a region-by-group dissociation in the dorsal and the ventral inferior frontal cortex was consistent with previously reported dissociations in semantic and nonsemantic reading strategies. In contrast to these significant findings, the groupings did not differentiate subjects on the basis of either sex or handedness, nor did they segregate the subjects with right- versus left-lateralized reading activation. We therefore conclude that, of the variables tested, age and reading strategy were the most prominent source of variability in activation for reading familiar words aloud.

Investigating possible subtypes of schizophrenia patients and controls based on brain cortical thickness

Schizophrenia is a heterogeneous disease in which different dimensions could be associated with localized subtypes in cortical thickness of the brain. Subtypes in data that includes patients and controls could be associated with patient/control could associate with patient/control groupings. Testing for subtypes provides a non-parametric investigation of group differences. Cortical thickness maps, generated from magnetic resonance images of 96 patients with schizophrenia and 106 controls, were co-registered and corrected for age-related thinning. At multiple map locations, the number of (sub)types best explaining cortical thickness in the patients, the controls, and both combined was determined. Grey matter volumes of selected regions were measured. Both patients and controls, considered independently, were predominantly homogeneous in cortical thickness. The few bimodal regions were similar in both groups. The combined subjects' cortical thickness was bimodal over 34% of the cortical mantle and otherwise unimodal. Further probing of these bimodal regions showed that subjects tending to belong to thinner modes were significantly more likely to be patients, and grey matter volumes of most bimodal regions were significantly smaller in patients. The study found no subtypes specific to patients. It suggested, however, that associations between abnormally thin cortex and schizophrenia are more widespread than shown by previously published results based on significance testing.

Inter-subject variability in the use of two different neuronal networks for reading aloud familiar words

Cognitive models of reading predict that high frequency regular words can be read in more than one way. We investigated this hypothesis using functional MRI and covariance analysis in 43 healthy skilled readers. Our results dissociated two sets of regions that were differentially engaged across subjects who were reading the same familiar words. Some subjects showed more activation in left inferior frontal and anterior occipito-temporal regions while other subjects showed more activation in right inferior parietal and left posterior occipito-temporal regions. To explore the behavioural correlates of these systems, we measured the difference between reading speed for irregularly spelled words relative to pseudowords outside the scanner in fifteen of our subjects and correlated this measure with fMRI activation for reading familiar words. The faster the lexical reading the greater the activation in left posterior occipito-temporal and right inferior parietal regions. Conversely, the slower the lexical reading the greater the activation in left anterior occipito-temporal and left ventral inferior frontal regions. Thus, the double dissociation in irregular and pseudoword reading behaviour predicted the double dissociation in neuronal activation for reading familiar words. We discuss the implications of these results which may be important for understanding how reading is learnt in childhood or re-learnt following brain damage in adulthood.

Group analysis and the subject factor in functional magnetic resonance imaging: analysis of fifty right-handed healthy subjects in a semantic language task

Before considering a given fMRI paradigm as a valid clinical tool, one should first assess the reliability of functional responses across subjects by establishing a normative database and defining a reference activation map that identifies major brain regions involved in the task at hand. However, the definition of such a reference map can be hindered by inter-individual functional variability. In this study, we analysed functional data obtained from 50 healthy subjects during a semantic language task to assess the influence of the number of subjects on the reference map and to characterise inter-individual functional variability. We first compared different group analysis approaches and showed that the extent of the activated network depends not only on the choice of the analysis approach but also on the statistical threshold used and the number of subjects included. This analysis suggested that, while the RFX analysis is suitable to detect confidently true positive activations, the other group approaches are useful for exploratory investigations in small samples. The application of quantitative measures at the voxel and regional levels suggested that while approximately 15-20 subjects were sufficient to reveal reliable and robust left hemisphere activations, >30 subjects were necessary for revealing more variable and weak right hemisphere ones. Finally, to visualise inter-individual variability, we combined two similarity indices that assess the percentages of true positive and false negative voxels in individual activation patterns relative to the group map. We suggest that these measures can be used for the estimation of the degree of 'normality' of functional responses in brain-damaged patients, where this question is often raised, and recommend the use of different quantifications to appreciate accurately the inter-individual functional variability that can be incorporated in group maps.

Detecting subject-specific activations using fuzzy clustering

Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner.

Graphical-model-based multivariate analysis of functional magnetic-resonance data

We propose a method for the analysis of functional magnetic-resonance (fMR) data, based on a Bayesian-network representation. Our method identifies multivariate linear/nonlinear voxel-activation pattern differences across groups, which may provide information complementary to that resulting from a general linear model (GLM)-based analysis. In addition, we describe a model-stabilization method based on data resampling, which may be helpful in the presence of small numbers of subjects, or when data are noisy.

Interindividual uniformity and variety of the “Writing center”: A functional MRI study

Our aim is to investigate the neural substrates for writing using fMRI (twenty right-handed subjects). We assumed that common areas involved in both writing with right and left hands are crucial to the central process of writing. We employed Japanese phonograms (Kana), in which phoneme-grapheme conversion would be extremely simple. Brain activation was examined under three conditions: (1) written naming with the right hand (WR), (2) written naming with the left hand (WL), and (3) naming silently (NA). While the comparison of WR to NA (WR>NA) exhibited activation only in the left frontoparietal area, the WL>NA comparison exhibited broader activation than the WR>NA comparison, i.e., the left frontoparietal area except the motor and sensory areas and the right frontoparietal area. A conjunction analysis in SPM2 revealed common areas of activation across the WR>NA and WL>NA comparisons, which are assumed to be crucial to writing. In the group analysis, three areas were found to be activated: the posterior end of the left superior frontal gyrus, which is superior and posterior to Exner's center; the anterior part of the left superior parietal lobule; and the lower part of the anterior limb of the left supramarginal gyrus. In the single-subject analysis, whereas the first two of the above three areas were found to be crucial for writing in all individuals, an interindividual inconsistency of involvement with writing was observed in three areas: the lower part of the anterior limb of the left supramarginal gyrus (60% involved); the right frontal region (47%); and the right intraparietal sulcus (47%).