Echoes of Vision: Mental Imagery in the Human Brain

When you picture the face of a friend or imagine your dream house, you are using the same parts of your brain that you use to see. How does the same system manage to both accurately analyze the world around it and synthesize visual experiences without any external input at all? We approach this question and others by extending the well-established theory that the human visual system embodies a probabilistic generative model of the visual world. That is, just as visual features co-occur with one another in the real world with a certain probability (the feature “tree” has a high probability of occurring with the feature “green”), so do the patterns of activity that encode those features in the brain. With such a joint probability distribution at its disposal, the brain can not only infer the cause of a given activity pattern on the retina (vision), but can also generate the probable visual consequence of an assumed or remembered cause (imagery). The formulation of this model predicts that the encoding of imagined stimuli in low-level visual areas resemble the encoding of seen stimuli in higher areas. To test this prediction we developed imagery encoding models-a novel tool that reveals how the features of imagined stimuli are encoded in brain activity. We estimated imagery encoding models from brain activity measured while subjects imagined complex visual stimuli, and then compared these to visual encoding models estimated from a matched viewing experiment. Consistent with our proposal, imagery encoding models revealed changes in spatial frequency tuning and receptive field properties that made early visual areas during imagery more functionally similar to higher visual areas during vision. Likewise, signal and noise properties of the voxel activation between vision and imagery favor the generative model interpretation. Our results provide new evidence for an internal generative model of the visual world, while demonstrating that vision is just one of many possible forms of inference that this putative internal model may support

The brain connectome as a personalized biomarker of seizure outcomes after temporal lobectomy

ObjectiveWe examined whether individual neuronal architecture obtained from the brain connectome can be used to estimate the surgical success of anterior temporal lobectomy (ATL) in patients with temporal lobe epilepsy (TLE).MethodsWe retrospectively studied 35 consecutive patients with TLE who underwent ATL. The structural brain connectome was reconstructed from all patients using presurgical diffusion MRI. Network links in patients were standardized as Z scores based on connectomes reconstructed from healthy controls. The topography of abnormalities in linkwise elements of the connectome was assessed on subnetworks linking ipsilateral temporal with extratemporal regions. Predictive models were constructed based on the individual prevalence of linkwise Z scores >2 and based on presurgical clinical data.ResultsPatients were more likely to achieve postsurgical seizure freedom if they exhibited fewer abnormalities within a subnetwork composed of the ipsilateral hippocampus, amygdala, thalamus, superior frontal region, lateral temporal gyri, insula, orbitofrontal cortex, cingulate, and lateral occipital gyrus. Seizure-free surgical outcome was predicted by neural architecture alone with 90% specificity (83% accuracy), and by neural architecture combined with clinical data with 94% specificity (88% accuracy).ConclusionsIndividual variations in connectome topography, combined with presurgical clinical data, may be used as biomarkers to better estimate surgical outcomes in patients with TLE

Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging

<div><p>Rationale</p><p>Disruptions of brain anatomical connectivity are believed to play a central role in several neurological and psychiatric illnesses. The structural brain connectome is typically derived from diffusion tensor imaging (DTI), which may be influenced by methodological factors related to signal processing, MRI scanners and biophysical properties of neuroanatomical regions. In this study, we evaluated how these variables affect the reproducibility of the structural connectome.</p><p>Methods</p><p>Twenty healthy adults underwent 3 MRI scanning sessions (twice in the same MRI scanner and a third time in a different scanner unit) within a short period of time. The scanning sessions included similar T1 weighted and DTI sequences. Deterministic or probabilistic tractography was performed to assess link weight based on the number of fibers connecting gray matter regions of interest (ROI). Link weight and graph theory network measures were calculated and reproducibility was assessed through intra-class correlation coefficients, assuming each scanning session as a rater.</p><p>Results</p><p>Connectome reproducibility was higher with data from the same scanner. The probabilistic approach yielded larger reproducibility, while the individual variation in the number of tracked fibers from deterministic tractography was negatively associated with reproducibility. Links connecting larger and anatomically closer ROIs demonstrated higher reproducibility. In general, graph theory measures demonstrated high reproducibility across scanning sessions.</p><p>Discussion</p><p>Anatomical factors and tractography approaches can influence the reproducibility of the structural connectome and should be factored in the interpretation of future studies. Our results demonstrate that connectome mapping is a largely reproducible technique, particularly as it relates to the geometry of network architecture measured by graph theory methods.</p></div

The scatter plots demonstrate the relationship between link-wise graph theory metrics obtained from connectomes calculated from scanning session in time 1 (x-axis) and in time 2 (y-axis) within the same MRI scanner.

<p>The scale set for the x-axis is the same as for the y-axis for all graphs. The ICC between each pair of measurements is displayed below each scatter plot. Of note, the relationship between degrees was not assessed for probabilistic tractography given the low sparsity of networks generated from probabilistic methods, therefore leading to a ceiling degree effect.</p

The scatter plots demonstrate the relationship between link-wise graph theory metrics across different scanners (Time 1, Scanner in x-axis and Time 1 Scanner B in y-axis).

<p>The ICC between each pair of measurements is displayed below each scatter plot. Similarly, the relationship between degrees was not assessed for probabilistic tractography given the low sparsity of networks generated from probabilistic methods, therefore leading to a ceiling degree effect.</p

Average connectivity matrices from all subjects for each scanning session (Time 1 and 2 in Scanner A represent measures from the same MRI scanner in different time points, and Time 1 in Scanner B represents a third measure from a different MRI scanner).

<p>Each matrix element represents the weighted connectivity between the ROIs indicated by the column and by the row. The color bars indicate log(weighted connectivity).</p

Link-wise ICCs.

<p>Each matrix entry represents the ICC observed for the white matter link between the gray matter ROI in the row and the gray matter ROI in the column.</p

This figure demonstrates each connectome link represented by a line corresponding to the center of mass of the bundle of fibers associated with that link (estimated from deterministic tractography).

<p>Each link is color-coded based on its reproducibility per tractography approach and scanner usage. The colorbars indicate the link-wise ICC.</p