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Recurrent network model The neural architecture consists of one recurrent neural network arranged as in Fig 1a. Table 2 Description of the buffer algorithm used to simulate integration over a temporal horizon. Results We resume in the Table 3 below the different experiments that we have done to present our model.

Table 3 Table of the different experimental setups. RNN goal-driven control In this section, we study solely the RNN, decoupled from the associative map, in order to explain its behavior during goal-driven control. Raster plots of the input vectors injected to the RNN and its respective output vector for the first 60 iterations toward a target solution. Raster plots of four strategies found by the RNN to trigger the firing of neuron 14 in twenty iterations. Ten trajectories found till triggering of neuron Arm control by the recurrent network with a reinforcement signal.

Spiking recurrent network analysis In order to understand better the organization of the spiking recurrent network, we analyze its functional properties at the population level and its dynamics at the neuron level. Cluster analysis in RNN. Histogram of the neurons variability measured during exploration and their relative position found within the sequence for hundred trials.

Recursivity and bootstrapping In the previous section, we have investigated the control of a recurrent network by a reinforcement signal mechanism to drive its output dynamics to a desired goal as in Fig 1a. Habit learning of arm sequence We propose to re-use the experiment done on arm control in section 3.

BG training by the IPL neural network and convergence rate with respect to the number of exposure of targeting goals. Interactive coupling between the recurrent map and the associative map. Sequence length retrieval during self-driven and forced conditions.

Discussion We propose a framework based on a coupled recurrent spiking neuronal system that achieves to perform long sequential planning by controlling the amplitude level of the spiking neurons through reinforcement signals. Subsumed and complementary systems As there is evidence that suggests that although single actions can be selected without basal ganglia involvement, chains of actions seem to require the basal ganglia [ 77 ].

Multi-step computation While the IPL working memory provides, stores, and manipulates representations; the basal ganglia model maps current states to courses of action [ 83 ]. Gain modulatory control Our optimization technique is based on the control of the sub-threshold activity of the neurons. Dopaminergic optimization The neurons in the recurrent network have sparse connections to each other so that the system possesses a high number of spatio-temporal patterns and requires several steps to reach the desired configuration; this behavior corresponds to the characteristics of one working memory.

Neuromorphic computation and symbolic AI systems In comparison to computer memories, the human Working Memory has developed the ability to deal with uncertain data sets and to initiate flexible and robust decision making.

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Acknowledgments We thank Souheil Hanoune and Karl Friston as well as the reviewers for interesting comments and fruitful feedback on anterior versions of the paper. Data Availability All relevant data are within the paper. The neural representation of sequences: Kording K, Wolpert DM. Bayesian decision theory in sensorimotor control.

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Orban H, Wolpert DM. Representations of uncertainty in sensorimotor control.

Iterative free-energy optimization for recurrent neural networks (INFERNO)

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Original Research ARTICLE

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An experimental unification of reservoir computing methods. Mannella F, Baldassare G. Selection of cortical dynamics for motor behaviour by the basal ganglia. Self-organization of distributedly represented multiple behavior schemata in a mirror system: Sandamirskaya Y, Schoner G. An embodied account of serial order: How instabilities drive sequence generation.

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Chaotic itinerancy and its roles in cognitive neurodynamics. Metastability and functional integration in anisotropically coupled map lattices. Eur Phys J B. Laje R, Buonomano DV. Robust timing and motor patterns by taming chaos in recurrent neural networks. The human Turing machine: Primate basal ganglia activity in a precued reaching task: Neural correlates of decision variables in parietal cortex. Visuomotor transformations underlying arm movements toward visual targets: The Journal of neuroscience. Retrospective revaluation in sequential decision making: J Exp Psychol Gen.

Active maintenance in prefrontal area 46 creates distractor-resistant memory. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. How to build a brain: A neural architecture for biological cognition New York, NY: A neuronal device for the control of multi-step computations.

Probabilistic models of cognition: How to grow a mind: Salinas E, Sejnowski TJ. Gain modulation in the central nervous system: Where behavior, neurophysiology and computation meet. Botvinick M, Watanabe T. From Numerosity to Ordinal Rank: The Journal of Neuroscience.

Multiplicative computation in a visual neuron sensitive to looming. Blohm G, Crawford JD. Fields of Gain in the Brain. Considering these facts, subcortical network of mentalizing seems to be complex and huge, but it has not been fully understood. In previous studies, some researches focused on subcortical structural connectivity mediating mentalizing. They have implicated fronto-temporo-parieto subcortical network in mentalizing processing, including the arcuate fascicle AF , cingulum, superior longitudinal fascicle SLF and inferior fronto-occipital fascicle IFOF; Philippi et al.

Of these white matter pathway, the AF, SLF and IFOF are known to be related to low-level mentalizing processing, while the cingulum is linked with high-level mentalizing processing Philippi et al. Several neuroimaging studies have described the lateralization of mentalizing processes. Notably and in line with alternative findings, a rightward lateralization in emotional processing has been suggested by a number of studies Schwartz et al.

However, they also found that the left amygdala only was related to negative emotional processing. In short, although both the right and left hemispheres are involved in mentalizing and emotional processing, the right hemisphere seems to play a more significant role than the left one. Some recent studies have shown that both high-level and low-level mentalizing abilities decline with high possibility following glioma surgery Herbet et al. However, little is known about the postoperative recovery and long-term influence of craniotomy on mentalizing Herbet et al.

To date, emphasis has been placed on the importance of intraoperative functional preservation of emotional recognition, namely low-level mentalizing especially for right cerebral hemispheric glioma Duffau, , ; Yordanova et al. However, regarding higher levels of mentalizing, the necessity of preservation during surgery and possibility of postoperative recovery have not yet been demonstrated. In this study, we investigated the subcortical network of high-level mentalizing in patients with right cerebral hemispheric gliomas who underwent awake surgeries.

We only included patients with gliomas in the right hemisphere for the two following reasons. First, as described above, mentalizing processes seem to be lateralized on the right, and, second, preserving language function in the left hemisphere is a high priority in glioma surgery. The primary purpose of this study was to examine the neural networks underlying high-level mentalizing processing, while the secondary purpose was to investigate the usefulness of awake surgery in preserving the neural networks involved in social cognitive function, especially high-level mentalizing.

Patient characteristics are presented in Table 1.

To note, patients with recurrence at postoperative 6-month were excluded from this study. Written informed consent was obtained from all individual participants. This study was performed according to the guidelines of the Internal Review Board of the Kanazawa University, and was approved by the medical ethics committee at Kanazawa University No.

In this group, the regions with positive mapping that were elicited by direct electrical stimulation DES were preserved. Importantly, we resected the central part of the tumor in all patients to fulfill our oncological priorities and performed the intraoperative assessments on the extended resection using the HLM test. Additionally, we decided not to perform the intraoperative HLM test in three cases due to oncological reasons. No significant between-group differences, namely the with and without intraoperative HLM test group, in basic characteristics were observed: Moreover, 18 normal healthy volunteers aged During the task, 4—6 cards were presented at random in front of patients, who were asked to sort these cards into the correct order based on the story.

All patients were assessed three times: These assessments were performed by the same trained occupational therapist R. The data were collected from medical records retrospectively. Prior to the study, we studied the relationship between the scores between the WAIS PA and the high-level mentalizing test with 65 normal healthy volunteers aged The carton format HLM test we used was a typical task for high-level mentalizing, namely the false belief task Takamiya et al. The story is as follows: Mary put the doll on the ground because she saw a flower and wanted to pick it. In addition, we performed other neuropsychological examinations including processing speed, attention, visuospatial cognition, low-level mentalizing and executive function for each patient.

Structural MR images were acquired during the 3-month postoperative period. The images had been acquired using conventional high-resolution 3DT1-weighted sequences on a 3. Each reconstruction was first achieved by R. The Steel test, namely non-parametric multiple comparison analysis was used to compare time-series scores of patients preoperative, postoperative 1 week and postoperative 3 months with scores of healthy volunteers. Moreover, scores were compared among three groups: We used two kinds of tract-wise lesion symptom analysis.

Recently, using white matter atlas in neuro-imaging analyses become common Herbet et al. The first step of this analysis was to estimate the amount of white matter that was resected. For this purpose, each resection cavity map was overlaid with the recent diffusion tensor imaging based white matter fiber atlas from the group of Thiebault de Schotten Rojkova et al. Next, using MRIcron software 3 , we automatically computed the number of overlapping voxels with each associated fiber. Tract probabilities were more than the threshold of 0.

Raw test score was transformed into standard residuals in which age and educational level were entered as predictors. All data were analyzed using the statistical analysis software JMP Version To investigate the spatial location and probability of disconnection induced by DES and surgical resection, we used Tractotron software, as a part of the BCBToolkit 4.

Tracttron enables to measure the disconnected probability considering topological position of the lesion and the number of damaged voxels on specific tract. The software outputs on Excel file with a disconnected probability of each given tract. The atlas of white matter was obtained from a group of healthy controls Rojkova et al. This test provides an estimate of the disconnection of every voxel due to the lesioned volume of interest VOI.

Positive mapping sites were plotted on the normalized images using medical records and intraoperative movies, making use of conventional anatomical landmarks. To demonstrate the putative relationship between the high-level mentalizing accuracy and the location of the resection cavity, the VLSM analysis was performed as previously described using NPM software provided in the MRIcron package Kinoshita et al. The parametric t -test was chosen to generate the statistical maps.

The significant differences between with and without lesion were identified and presented as Z scores at MNI coordinates. To investigate the potential roles of the tracts, a standardized white matter atlas was used Rojkova et al.

All patients were operated using an asleep-awake-asleep technique with cortical and subcortical brain mapping achieved by electrical stimulation Duffau et al. After a dural incision, the cortical and subcortical areas were evaluated using DES, which was delivered via a bipolar probe 5-mm tip with a biphasic current pulse frequency, 60 Hz; single-pulse phase duration, 1 ms; amplitude of biphasic current, 3—6 mA.

Several tasks were performed intraoperatively, and were selected carefully considering optimal onco-functional balance in each patient e. Operative view and the intraoperative assessments were recorded on a hard disk via video camera. In the intraoperative mentalizing test, we tried to perform both low- and HLM tests to optimize the postoperative social lives of the patients.

However, certain criteria, including preoperative neuropsychological examinations and intraoperative patient condition assessments, are required to perform these intraoperative neuropsychological assessments. Generally, because the HLM test is more difficult than the low-level test, the HLM test is more difficult to perform intraoperatively.

Therefore, sometimes we decided to only perform the low-level mentalizing task during surgery. The typical task of high-level mentalizing, namely the false belief task, was used as the intraoperative HLM test as mentioned above Takamiya et al. The method of presentation was adapted for awake surgery as described previously Sarfati et al.

Next, two choices were presented on the fourth strip, and patients had to choose which one of the two answers was the most logical to complete the comic strip sequence. Cortical or subcortical areas were stimulated directly when 2nd and 3rd strips are presented. Since intraoperative assessment should be uncomplicated and patients should certainly give correct response in normal condition, we decided to use the simple HLM test for intraoperative task, which is not same as pre- and post-operative assessment tool.

Accuracy of high-level mentalizing in patients was lower than that of healthy volunteers before surgery and 1 week after surgery Figure 1. However, at 3 months after surgery, there was no significant difference between the score of all patients and healthy volunteers. In the comparison of three groups, scores of both the with and without intraoperative HLM test groups were lower than that of healthy volunteers at preoperative and postoperative 1 week Table 2.

Notably, at postoperative 3 months, the normalization of the score was observed only in the with intraoperative HLM test group. In addition, of the six patients whose high-level mentalizing were disturbed, three showed low-level mentalizing deficit at postoperative 1 week.

At the chronic phase, low-level mentalizing deficit was observed in only one patients, while high-level mentalizing deficit remained in three patients Supplementary Table S2. A Steel analysis non-parametric multiple comparison analysis was performed. Scores of all patients at preoperative and postoperative 1 week were significantly lower than those of healthy volunteers, while there was no difference between groups at postoperative 3 months.

The greatest overlap of resected region was equivalent to the course of the frontal aslant tract FAT and fronto-striatal tract FST. Overlap map of resected region. Overlap map of resection cavities shows that the right prefrontal cortex was the region with the greatest overlap. As for subcortical level, the greatest overlap region was equivalent to the course of the frontal-aslant tract FAT and fronto-striatal tract FST.

The TBLS analysis was used to analyze the relationship between the resected lesion volumes in each association pathway voxels and the accuracy of the high-level mentalizing Table 3 , left row. Correlations between social cognition accuracy and damage to white matter tracts. However, neither correlation remained significant after the Bonferroni correction. Thus, it is interesting that only the FST was significantly correlated with high-level mentalizing in both analyses. PL is defined as follows:. If a network is fully connected hence more efficient , then PL will be 1; otherwise it will be greater than 1.

We define global efficiency as the reciprocal of PL. Consistent with Roy and colleagues [ 53 ], we define the cost of each functional edge as the product of the Euclidean distance between the ROI-pair it connects and the absolute weight of the connection.

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The overall network cost is determined as the total cost for all edges within the network. Therefore, both network and nodal cost are calculated considering three components: This allows us to examine whole-brain networks for cost as well as to isolate the most costly brain subsystems when comparing groups. Fig 3 illustrates the components included during the static and dynamic network analysis.

Primary graph metrics included PL and local and global clustering coefficiency see Table 6. To examine the reliability of dynamic meta-states after injury Hypothesis 1 , we correlated the frequency and number of state transitions between Run 1 and Run 2 in each sample. Fig 4A—4C reveal that at the individual subject level the frequency for entering a given state during Run1 was highly predictive of the frequency for that state in Run 2 r-values ranging from 0. Regarding the reliability in state frequency, these data are important and confirm within-subject reliability in observable brain states over time.

However, the lack of reliability in transition data in the HC sample are interesting and possibly point to less dynamic range in the TBI sample compared to the HC sample. Given that the number of possible states k is investigator determined, we examined directly the influence of k on state transitions. Fig 5 reveals consistent results for fewer state transitions in TBI irrespective of the number of states permitted at the first step.

These findings regarding reduced state transitions are discussed in detail below. For both samples, the frequency for a state during run 1 was highly predictive of the frequency for that state in run 2. Transitions in run1 predict transitions in run 2 only for the TBI sample. The TBI sample reliably shows reduced transitions irrespective of investigator decisions regarding k. Error bars indicate standard error.

The adjacency matrices for the least visited states 1, 2, and 4 are presented in Fig 6. Adjacency matrices for states 1, 2, and 4 which appeared only in the TBI sample. Given the finding that states 5 and 6 differentiated the groups, we correlated mean frequency for these two states with three behavioral outcomes variables in TBI: Given the high frequency of state 5 in HCs and that this state was an important differentiator between groups, we examined the relationship between the mean cost for state 5 hubs see Fig 11 and the frequency for state 5.

That is, as part of the second hypothesis, we aimed to determine if state-level dynamics could be driven by the connectivity of the most highly connected nodes. Only partial support for this hypothesis was revealed. Of the three hubs of interest, nodes in the SN showed some relationship with state 5 frequency, whereas nodes in the DMN and ECN were not strong predictors or drivers of state 5 frequency Table 8. Cost probability distributions for state 3.

Distributions based upon the cost for all nodes collapsed across runs for each group. Hubs identified for each of the samples for state 3. Cost probability distributions for state 5. Hubs identified for each of the samples for state 5. Cost probability distributions for state 6. Distributions based upon the cost for all notes collapsed across runs for each group. Hubs identified for each of the samples for state 6. Average cost for hubs for run 1 and run 2 and then the average between runs. To determine if the network cost associated with state 5 hubs predicted behavior, correlational analysis was conducted using the three simple visual scanning tasks mDSST, Trails A, VSAT and hubs established state 5.

These three hubs showed inconsistent relationships with comparisons revealing small to medium effect sizes, with the hubs of the SN and ECN showing some prediction of behavior and no relationship between DMN and behavior. Overall, network hubs in the DMN, SN, and ECN were generally poor to modest predictors of performance on tests of information processing efficiency collected inside and outside the scanner see Table 9.

Examining the relationship between cost in three hubs in a priori networks and behavioral variance on the in-scanner task mDSST in the TBI sample Note: One important goal in this study was to determine the predictors of network variability, or state transitions, and the relationship between network variability and performance. Subtle differences were evident when comparing the number of state transitions between the TBI and HC groups.

To examine the consistency of these results we directly examined the influence of k on state transitions and the TBI sample consistently showed reduced network dynamics see Fig 5. We interpret these data to be indicative of reliably reduced state transitions in TBI. This finding does not support the hypothesis that brain injury results in increased network variability. The findings are presented in Table 10 top , but overall, the number of run 1 transitions was a modest predictor of network dynamics for variance in performance for both behavioral runs.

That is, early network transitions predicted variance in performance for run 1 and run 2. The relationship between the number of state transitions and variability on in-scanner task performance. The relationship between state transitions and mean nodal degree total network connectivity. Transitions during the first run is a predictor of variance for both runs, whereas state transitions during the second run did not predict behavioral variance.

Lastly, it was a goal to determine if local or global connectivity predicted the number of transitions. Based upon the static network cost we examined the mean connectivity of the top five nodes i. Regional influences maintained no relationship with state transitions r-values ranging from 0. The current study used a graph theoretical framework and dynamic connectivity modeling to examine functioning of large-scale neural networks after head trauma.

We used two runs of task-related data collection in order to examine the reliability of the findings. The following discussion is focused on several key points. Third, it appears that the TBI sample may be less likely to transition between states and the number of transitions during run1 was a modest predictor of performance variability for both runs. Finally, the network variance identified as state transitions was predicted by global connectivity, indicating that the increased connectivity commonly observed in TBI may be a source of reduced network variability.

We discuss the implications these findings have for understanding large-scale neural network changes occurring after significant neurological disruption. There is a growing literature demonstrating the unique role of network hubs as driving brain dynamics toward specific states [ 15 , 23 , 54 , 55 ]. In Hypothesis one, we anticipated that network hubs would predict the frequency for dynamic connectivity states.

We focused the analysis on state 5 given its relatively high frequency and that it was one state that differentiated the groups. Five hubs were examined and collapsed into three distinct networks: These findings revealed that, within individuals with TBI, mean degree for state 5 hubs maintained a positive correlation with state 5 frequency for run 1 but not for run 2. Given our goal to use the two runs of data to demonstrate the reliability of findings, interpreting this finding is difficult. If we focus on possible effects of chronology, hubs may serve as the backbone for information transfer during run 1, thus driving activity in state 5, but the network requirement for this influence diminishes over time.

Similar network dynamics have been observed in schizophrenia where early measures of network flexibility during early runs predict concurrent and later network functioning including behavioral performance [ 56 ]. While our goal in this study was to focus on cortical hubs as drivers of network states post injury, one unexpected finding worth revisiting was the repeated observation that crus 1 and II functioned as a hub within the network. This was evident for all three states showing the highest frequency post TBI and in state 6, this finding was unique to the TBI sample.

The cerebellum is involved in a number of functions including timing and circadian rhythms, associative learning mechanisms, and higher level cognitive processing see [ 21 , 57 ] for meta-analytical and theoretical reviews. In particular, Crus I and II have been consistently linked to roles in information processing e. We anticipate that further investigation here is worthwhile given that the history of findings of enhanced cerebellar response in recent network connectivity modeling in both moderate and severe TBI [ 58 ] and mild TBI [ 17 , 59 ] and even altered cerebellar response as a primary indicator of response to methylphenidate intervention to improve cognition in TBI [ 32 , 60 ].

The primary prediction that brain injury results in greater variability i. Therefore, the range of network expression was more restricted in the TBI sample with respect to: Given the established literature documenting the increased variation in behavioral performance during tasks of cognition [ 61 ] and motor functioning [ 6 ] after injury, we expected that these behavioral deficits would be mirrored by more variable network dynamics operationalized here as transitions. In other words, as the variability in performance increases, individuals with TBI were less likely to show state transitions.

Other investigators have reported similar results after injury demonstrating that after TBI, higher brain signal variability may be predictive of cognitive recovery [ 62 ] and that brain injury results in reduced network variability [ 63 ]. Recent work based in control theory used simulations demonstrate a limited dynamic range of states available to individuals with mild TBI [ 64 ]. The loss of nodal specificity may result in incorporation of additional resources or engagement of alternative auxiliary pathways [ 24 , 66 ] or hubs see Fig 10 in TBI resulting in less freedom for expression of network dynamics and greater susceptibility to neural noise [ 64 ].

Finally, while there were subtle relationships between the states differentiating the two groups states 5 and 6 , the relationship between network dynamics and cognitive outcome does not appear to be straight-forward. Future work should be organized around modulating variation in performance using task-load manipulations to determine the contributors to network states arising during task that may account for performance variability.

Similar analyses might be extended to other clinical disorders, such as multiple sclerosis, where the diffuse effects of pathophysiology have been shown to result in increased performance variability. One final observation is that four of the 23 subjects with TBI showed zero frequency of time spent in states 5 and 6 and very little to no frequency in state 3.

These subjects occupied states 1, 2, and 4 and three of the four showed zero transitions between states. In each case, occupation of these rare brain states was true for both runs so the finding was reliable, but given the small numbers observed in these states, it is possible that these three rare states were occupied solely TBI cases as opposed to HC cases only by chance. If allowed to interpret the networks for these rare cases, states 1 and 4 were characterized by high overall network cost with several regions showing very high connectivity including the ECN, SN, and visual networks See Fig 6.

The reason for the emergence of these states in this TBI sub-group is not clear, but based upon clinical MRI at the time of injury, there was significant disruption of frontal systems in these four cases. Additional work will be needed to determine if state-level analyses reveal sub-types within TBI that emerge as distinct network responses to injury or if these states are not related to pathology and are also visible in HCs.

The current approach provides an important opportunity to examine whole-brain connectivity over multiple time scales. While we report the first set of findings related to the local and global changes in network connectivity and cost in moderate and severe TBI, this study is not without limitations.

First, like most all studies in this literature, TBI is a heterogeneous disorder and ideally we would have a sample size that permitted subgroups for analysis. While the current data demonstrated the within subject reliability of network dynamics and the sample size here is comparable to prior graph theory analysis examining static networks after moderate and severe TBI [ 4 , 6 , 7 , 68 ], the sample size for this study does preclude direct examination of the reliability of these findings with respect to the groups e.

For this reason, replication of the current findings is needed in a separate group of individuals with moderate and severe TBI with focus on the primary findings: Second, ICA has distinct advantages both with respect to reduction of the influence of nuisance signal 17 and avoiding signal averaging across heterogeneous signals. However, there are inherent limitations to a brain parcellation of 44 network nodes in particular with respect to subtle effects within subnetworks e. To the degree that BOLD fMRI is sensitive to these subtle differences, group-level ICA may not detect more nuanced effects where the network nodes are changing as well as the between-node interactions.

However, we anticipate that ICA remains an ideal statistical application given the focus in this study on large-scale network dynamics, including observing the most reliable networks e. In summary, this study supports the reliability of examining dynamic network states after neurological disruption. It also supports prior work demonstrating the potentially unique role of some network hubs in motivating network states. Finally, the possible loss of network dynamics after TBI may be a predictor of greater behavioral variability. This link between network dynamics and behavioral outcome in TBI appears to be an important future line of investigation.

We want to thank Dr. Arnab Roy for his support of this work and for his mentorship of the first author during analysis and manuscript preparation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. Published online Jun 8. Rajtmajer , Formal analysis , Methodology , 5 and Frank G. Author information Article notes Copyright and License information Disclaimer.

Received Oct 24; Accepted May 2. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders.

Study goals and hypotheses Our objective is to understand the influence TBI has on large-scale neural networks with focus on distinct brain states assessed using dynamic functional connectivity dFC approach. Materials and methods Subjects included 23 individuals who sustained moderate to severe TBI and 19 healthy controls of comparable age and education see Table 1 for demographic information. Table 1 Demographic information. Open in a separate window. Table 2 Injury severity, mechanism of injury and brain imaging findings for all TBI subjects.

S Glasgow Coma Scale score Mechanism of Injury Injury Characteristics 1 7 Fall Edema in frontal pole bilateral, paracingulate gyrus, and bilateral cerebral white matter 2 unknown MVA No acute findings; A few weeks post injury SPECT and PET scans revealed bilateral frontal and temporal lobe and cerebellar findings 3 unknown unknown Small to moderate left temporo-parietal subdural hematoma with pneumocephalus.

Diffuse left cerebral edema and small subarachnoid blood along the superior interhemispheric falx. Tractor trailor Intraventricular hemorrhage in bilateral lateral ventricles right greater than left , the left temporal horn, the third and the right fourth ventricles. Punctate bifrontal contusions and hyperdensity adjacent to the temporal horn and adjacent to the falx is consistent with contusion.

There is no evidence of acute bony fracture. Intraventricular hemorrhage in occipital horns bilaterally, left greater than right. Small subdural along the posterior interhemispheric fissure and tentorium. There is mild sulcal enlargement advanced for age of the patient. Subarachnoid hemorrhage in right posterior frontal and temporal lobes. Cerebral contusion in bilateral frontal lobes, left parietal lobe and left temporal lobe. Nearly complete effacement of the convexity sulci, sylvian fissures resulting in subtle shift to left.

Bifrontal subcortical foci of hyperdensity suggesting DAI. Left intraventricular choroid acute hematoma. A small left frontal parasagittal hyperdensity is seen also suggesting of a subdural acute hematoma. Small scattered punctate foci of intraparenchymal hypodensities throughout the cerebral hemispheres e. Midline shift measuring approximately 4 mm. Left temporal bone fracture. Behavioral data All study participants were administered a neuropsychological battery of tests to assess level of cognitive functioning. Table 3 Neuropsychological performance of TBI group. Mean sd raw score.