Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease.

Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.

Individual connectivity-based parcellations reflect functional properties of human auditory cortex.

Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

Personalized brain MRI revealed distinct functional and anatomical disruptions in Creutzfeldt-Jakob disease and Alzheimer's disease.

AIMS: Creutzfeldt-Jakob disease (CJD) is a lethal neurodegenerative disorder, which leads to a rapidly progressive dementia. This study aimed to examine the cortical alterations in CJD, changes in these brain characteristics over time, and the differences between CJD and Alzheimer's disease (AD) that show similar clinical manifestations. METHODS: To obtain reliable, subject-specific functional measures, we acquired 24 min of resting-state fMRI data from each subject. We applied an individual-based approach to characterize the functional brain organization of 10 patients with CJD, 8 matched patients with AD, and 8 normal controls. We measured cortical atrophy as well as disruption in resting-state functional connectivity (rsFC) and then investigated longitudinal brain changes in a subset of CJD patients. RESULTS: CJD was associated with widespread cortical thinning and weakened rsFC. Compared with AD, CJD showed distinct atrophy patterns and greater disruptions in rsFC. Moreover, the longitudinal data demonstrated that the progressive cortical thinning and disruption in rsFC mainly affected the association rather than the primary cortex in CJD. CONCLUSIONS: CJD shows unique anatomical and functional disruptions in the cerebral cortex, distinct from AD. Rapid progression of CJD affects both the cortical thickness and rsFC in the association cortex.

Personalized functional imaging-guided rTMS on the superior frontal gyrus for post-stroke aphasia: A randomized sham-controlled trial.

BACKGROUND: Aphasia affects approximately one-third of stroke patients and yet its rehabilitation outcomes are often unsatisfactory. More effective strategies are needed to promote recovery. OBJECTIVE: We aimed to examine the efficacy and safety of the theta-burst stimulation (TBS) on the language area in the superior frontal gyrus (SFG) localized by personalized functional imaging, in facilitating post-stroke aphasia recovery. METHODS: This randomized sham-controlled trial uses a parallel design (intermittent TBS [iTBS] in ipsilesional hemisphere vs. continuous TBS [cTBS] in contralesional hemisphere vs. sham group). Participants had aphasia symptoms resulting from their first stroke in the left hemisphere at least one month prior. Participants received three-week speech-language therapy coupled with either active or sham stimulation applied to the left or right SFG. The primary outcome was the change in Western Aphasia Battery-Revised (WAB-R) aphasia quotient after the three-week treatment. The secondary outcome was WAB-R aphasia quotient improvement after one week of treatment. RESULTS: Ninety-seven patients were screened between January 2021 and January 2022, 45 of whom were randomized and 44 received intervention (15 in each active group, 14 in sham). Both iTBS (estimated difference = 14.75, p < 0.001) and cTBS (estimated difference = 13.43, p < 0.001) groups showed significantly greater improvement than sham stimulation after the 3-week intervention and immediately after one week of treatment (p's < 0.001). The adverse events observed were similar across groups. A seizure was recorded three days after the termination of the treatment in the iTBS group. CONCLUSION: The stimulation showed high efficacy and SFG is a promising stimulation target for post-stroke language recovery.

Focused ultrasound thalamotomy for tremor treatment impacts the cerebello-thalamo-cortical network.

High-intensity Magnetic Resonance-guided Focused Ultrasound (MRgFUS) is a recent, non-invasive line of treatment for medication-resistant tremor. We used MRgFUS to produce small lesions in the thalamic ventral intermediate nucleus (VIM), an important node in the cerebello-thalamo-cortical tremor network, in 13 patients with tremor-dominant Parkinson's disease or essential tremor. Significant tremor alleviation in the target hand ensued (t(12) = 7.21, p < 0.001, two-tailed), which was strongly associated with the functional reorganization of the brain's hand region with the cerebellum (r = 0.91, p < 0.001, one-tailed). This reorganization potentially reflected a process of normalization, as there was a trend of increase in similarity between the hand cerebellar connectivity of the patients and that of a matched, healthy control group (n = 48) after treatment. Control regions in the ventral attention, dorsal attention, default, and frontoparietal networks, in comparison, exhibited no association with tremor alleviation and no normalization. More broadly, changes in functional connectivity were observed in regions belonging to the motor, limbic, visual, and dorsal attention networks, largely overlapping with regions connected to the lesion targets. Our results indicate that MRgFUS is a highly efficient treatment for tremor, and that lesioning the VIM may result in the reorganization of the cerebello-thalamo-cortical tremor network.

Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients.

Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.

Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder.

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous syndrome and can be conceptualized as a mixture of dimensional abnormalities across several specific brain circuits. The neural underpinnings of different symptom dimensions in MDD are not well understood. We aimed to identify robust, generalizable, functional connectivity (FC)-based biomarkers for different symptom dimensions in MDD using individualized functional connectomes. METHODS: Patterns of FC associated with symptom severity were identified using a novel, individualized, functional network parcellation analysis in conjunction with hierarchical clustering. Dimension-specific prediction models were trained to estimate symptom severity in first-episode medication-naïve patients (discovery dataset, n = 95) and replicated in an independent validation dataset (n = 94). The correlation between FC changes and symptom changes was further explored in a treatment dataset (n = 55). RESULTS: Two distinct symptom clusters previously identified in patients with MDD, namely dysphoric and anxiosomatic clusters, were robustly replicated in our data. A connectivity biomarker associated with dysphoric symptoms was identified, which mainly involved the default, dorsal attention, and limbic networks. Critically, this brain-symptom association was confirmed in the validation dataset. Moreover, the marker also tracked dysphoric symptom improvement following a 2-week antidepressant treatment. For comparison, we repeated our analyses using a nonindividualized approach and failed to identify replicable brain-symptom biomarkers. Further quantitative analysis indicated that the generalizability of the connectivity-symptom association was hampered when functional regions were not localized in individuals. CONCLUSIONS: This work reveals robust, replicable FC biomarkers for dysphoric symptoms in MDD, demonstrates the advantage of individual-oriented approach, and emphasizes the importance of independent validation in psychiatric neuroimaging analysis.

Fast cortical surface reconstruction from MRI using deep learning.

Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the "level set representation". A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject's cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test-retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.

Co-activation patterns across multiple tasks reveal robust anti-correlated functional networks.

Whether antagonistic brain states constitute a fundamental principle of human brain organization has been debated over the past decade. Some argue that intrinsically anti-correlated brain networks in resting-state functional connectivity are an artifact of preprocessing. Others argue that anti-correlations are biologically meaningful predictors of how the brain will respond to different stimuli. Here, we investigated the co-activation patterns across the whole brain in various tasks and test whether brain regions demonstrate anti-correlated activity similar to those observed at rest. We examined brain activity in 47 task contrasts from the Human Connectome Project (N = 680) and found robust antagonistic interactions between networks. Regions of the default network exhibited the highest degree of cortex-wide negative connectivity. The negative co-activation patterns across tasks showed good correspondence to that derived from resting-state data processed with global signal regression (GSR). Interestingly, GSR-processed resting-state data was a significantly better predictor of task-induced modulation than data processed without GSR. Finally, in a cohort of 25 patients with depression, we found that task-based anti-correlations between the dorsolateral prefrontal cortex (DLPFC) and subgenual anterior cingulate cortex were associated with clinical efficacy of transcranial magnetic stimulation therapy targeting the DLPFC. Overall, our findings indicate that anti-correlations are a biologically meaningful phenomenon and may reflect an important principle of functional brain organization.

Structural correlates underlying accelerated magnetic stimulation in Parkinson's disease.

Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive neuromodulation technique with great potential in the treatment of Parkinson's disease (PD). This study aimed to investigate the clinical efficacy of accelerated rTMS and to understand the underlying neural mechanism. In a double-blinded way, a total of 42 patients with PD were randomized to receive real (n = 22) or sham (n = 20) continuous theta-burst stimulation (cTBS) on the left supplementary motor area (SMA) for 14 consecutive days. Patients treated with real cTBS, but not with sham cTBS, showed a significant improvement in Part III of the Unified PD Rating Scale (p < .0001). This improvement was observed as early as 1 week after the start of cTBS treatment, and maintained 8 weeks after the end of the treatment. These findings indicated that the treatment response was swift with a long-lasting effect. Imaging analyses showed that volume of the left globus pallidus (GP) increased after cTBS treatment. Furthermore, the volume change of GP was mildly correlated with symptom improvement and associated with the baseline fractional anisotropy of SMA-GP tracts. Together, these findings implicated that the accelerated cTBS could effectively alleviate motor symptoms of PD, maybe by modulating the motor circuitry involving the SMA-GP pathway.

Reconstructing lost BOLD signal in individual participants using deep machine learning.

Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two healthy datasets and in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions capture individual-specific information. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individual's own functional brain organization.

Subthalamic Nucleus Deep Brain Stimulation Modulates 2 Distinct Neurocircuits.

OBJECTIVE: Current understanding of the neuromodulatory effects of deep brain stimulation (DBS) on large-scale brain networks remains elusive, largely due to the lack of techniques that can reveal DBS-induced activity at the whole-brain level. Using a novel 3T magnetic resonance imaging (MRI)-compatible stimulator, we investigated whole-brain effects of subthalamic nucleus (STN) stimulation in patients with Parkinson disease. METHODS: Fourteen patients received STN-DBS treatment and participated in a block-design functional MRI (fMRI) experiment, wherein stimulations were delivered during "ON" blocks interleaved with "OFF" blocks. fMRI responses to low-frequency (60Hz) and high-frequency(130Hz) STN-DBS were measured 1, 3, 6, and 12 months postsurgery. To ensure reliability, multiple runs (48 minutes) of fMRI data were acquired at each postsurgical visit. Presurgical resting-state fMRI (30 minutes) data were also acquired. RESULTS: Two neurocircuits showed highly replicable, but distinct responses to STN-DBS. A circuit involving the globus pallidus internus (GPi), thalamus, and deep cerebellar nuclei was significantly activated, whereas another circuit involving the primary motor cortex (M1), putamen, and cerebellum showed DBS-induced deactivation. These 2 circuits were dissociable in terms of their DBS-induced responses and resting-state functional connectivity. The GPi circuit was frequency-dependent, selectively responding to high-frequency stimulation, whereas the M1 circuit was responsive in a time-dependent manner, showing enhanced deactivation over time. Finally, activation of the GPi circuit was associated with overall motor improvement, whereas M1 circuit deactivation was related to reduced bradykinesia. INTERPRETATION: Concurrent DBS-fMRI using 3T revealed 2 distinct circuits that responded differentially to STN-DBS and were related to divergent symptoms, a finding that may provide novel insights into the neural mechanisms underlying DBS. ANN NEUROL 2020;88:1178-1193.

Patterns of motor recovery and structural neuroplasticity after basal ganglia infarcts.

OBJECTIVE: To elucidate the timeframe and spatial patterns of cortical reorganization after different stroke-induced basal ganglia lesions, we measured cortical thickness at 5 time points over a 6-month period. We hypothesized that cortical reorganization would occur very early and that, along with motor recovery, it would vary based on the stroke lesion site. METHODS: Thirty-three patients with unilateral basal ganglia stroke and 23 healthy control participants underwent MRI scanning and behavioral testing. To further decrease heterogeneity, we split patients into 2 groups according to whether or not the lesions mainly affect the striatal motor network as defined by resting-state functional connectivity. A priori measures included cortical thickness and motor outcome, as assessed with the Fugl-Meyer scale. RESULTS: Within 14 days poststroke, cortical thickness already increased in widespread brain areas (p = 0.001), mostly in the frontal and temporal cortices rather than in the motor cortex. Critically, the 2 groups differed in the severity of motor symptoms (p = 0.03) as well as in the cerebral reorganization they exhibited over a period of 6 months (Dice overlap index = 0.16). Specifically, the frontal and temporal regions demonstrating cortical thickening showed minimal overlap between these 2 groups, indicating different patterns of reorganization. CONCLUSIONS: Our findings underline the importance of assessing patients early and of considering individual differences, as patterns of cortical reorganization differ substantially depending on the precise location of damage and occur very soon after stroke. A better understanding of the macrostructural brain changes following stroke and their relationship with recovery may inform individualized treatment strategies.

Functional connectivity underpinnings of electroconvulsive therapy-induced memory impairments in patients with depression.

Electroconvulsive therapy (ECT) is an effective treatment for severe medication-resistant depression. However, ECT frequently results in episodic memory impairments, causing many patients to discontinue treatment. The objective of this study was to explore the functional connectivity underpinnings of ECT-induced episodic memory impairments. We investigated verbal episodic memory and intrinsic functional connectivity in 24 patients with depression (13F, 11M) before and after ECT, and 1 month after treatment. We used a novel individual-oriented approach to examine functional connectivity, and trained a linear support vector regression model to estimate verbal memory performance based on connectivity. The model identified a set of brain connections that can predict baseline verbal memory performance (r = 0.535, p = 0.026). Importantly, we found a nonoverlapping set of brain connections whose changes after ECT can track patients' verbal memory impairments (r = 0.613, p = 0.008). These connections mainly involve the frontoparietal control, default mode, and hippocampal networks, suggesting that ECT affects broad functional networks that are involved in memory performance. In contrast, functional connectivity defined using traditional group-level analyses was unable to estimate either baseline memory performance or post-ECT verbal memory impairments. A parallel analysis using the same strategy did not identify a connectivity marker for overall mood improvement, suggesting that functional connectivity changes related to depressive symptoms may be highly heterogenous. Our findings shed light on the mechanism through which ECT impairs episodic memory, and additionally underline the importance of accounting for interindividual variability in the investigation of functional brain organization in patients with depression.

Habitual use of GPS negatively impacts spatial memory during self-guided navigation.

Global Positioning System (GPS) navigation devices and applications have become ubiquitous over the last decade. However, it is unclear whether using GPS affects our own internal navigation system, or spatial memory, which critically relies on the hippocampus. We assessed the lifetime GPS experience of 50 regular drivers as well as various facets of spatial memory, including spatial memory strategy use, cognitive mapping, and landmark encoding using virtual navigation tasks. We first present cross-sectional results that show that people with greater lifetime GPS experience have worse spatial memory during self-guided navigation, i.e. when they are required to navigate without GPS. In a follow-up session, 13 participants were retested three years after initial testing. Although the longitudinal sample was small, we observed an important effect of GPS use over time, whereby greater GPS use since initial testing was associated with a steeper decline in hippocampal-dependent spatial memory. Importantly, we found that those who used GPS more did not do so because they felt they had a poor sense of direction, suggesting that extensive GPS use led to a decline in spatial memory rather than the other way around. These findings are significant in the context of society's increasing reliance on GPS.

Individual-Specific Analysis for Psychoradiology.

In neuroimaging research, averaging data at the level of the group results in blurring of potentially meaningful individual differences. A more widespread use of an individual-specific approach is advocated for, which involves a more thorough investigation of each individual in a group, and characterization of idiosyncrasies at the level of behavior, cognition, and symptoms, as well as at the level of brain organization. It is hoped that such an approach, focused on individuals, will provide convergent findings that will help identify the underlying pathologic condition in various psychiatric disorders and help in the development of treatments individualized for each patient.

Fimbria-Fornix Volume Is Associated With Spatial Memory and Olfactory Identification in Humans.

White matter pathways that surround the hippocampus comprise its afferent and efferent connections, and are therefore crucial in mediating the function of the hippocampus. We recently demonstrated a role for the hippocampus in both spatial memory and olfactory identification in humans. In the current study, we focused our attention on the fimbria-fornix white matter bundle and investigated its relationship with spatial memory and olfactory identification. We administered a virtual navigation task and an olfactory identification task to 55 young healthy adults and measured the volume of the fimbria-fornix. We found that the volume of the right fimbria-fornix and its subdivisions is correlated with both navigational learning and olfactory identification in those who use hippocampus-based spatial memory strategies, and not in those who use caudate nucleus-based navigation strategies. These results are consistent with our recent finding that spatial memory and olfaction rely on similar neural networks and structures.

An intrinsic association between olfactory identification and spatial memory in humans.

It was recently proposed that olfaction evolved to aid navigation. Consistent with this hypothesis, olfactory identification and spatial memory are linked to overlapping brain areas which include the orbitofrontal cortex and hippocampus. However, the relationship between these two processes has never been specifically investigated. Here, we show that olfactory identification covaries with spatial memory in humans. We also found that the cortical thickness of the left medial orbitofrontal cortex, and the volume of the right hippocampus, predict both olfactory identification and spatial memory. Finally, we demonstrate deficits in both olfactory identification and spatial memory in patients with lesions of the medial orbitofrontal cortex. Our findings reveal an intrinsic relationship between olfaction and spatial memory that is supported by a shared reliance on the hippocampus and medial orbitofrontal cortex. This relationship may find its roots in the parallel evolution of the olfactory and hippocampal systems.

Dissociable contributions of the prefrontal cortex to hippocampus- and caudate nucleus-dependent virtual navigation strategies.

The hippocampus and the caudate nucleus are critical to spatial- and stimulus-response-based navigation strategies, respectively. The hippocampus and caudate nucleus are also known to be anatomically connected to various areas of the prefrontal cortex. However, little is known about the involvement of the prefrontal cortex in these processes. In the current study, we sought to identify the prefrontal areas involved in spatial and response learning. We used functional magnetic resonance imaging (fMRI) and voxel-based morphometry to compare the neural activity and grey matter density of spatial and response strategy users. Twenty-three healthy young adults were scanned in a 1.5 T MRI scanner while they engaged in the Concurrent Spatial Discrimination Learning Task, a virtual navigation task in which either a spatial or response strategy can be used. In addition to increased BOLD activity in the hippocampus, spatial strategy users showed increased BOLD activity and grey matter density in the ventral area of the medial prefrontal cortex, especially in the orbitofrontal cortex. On the other hand, response strategy users exhibited increased BOLD activity and grey matter density in the dorsal area of the medial prefrontal cortex. Given the prefrontal cortex's role in reward-guided decision-making, we discuss the possibility that the ventromedial prefrontal cortex, including the orbitofrontal cortex, supports spatial learning by encoding stimulus-reward associations, while the dorsomedial prefrontal cortex supports response learning by encoding action-reward associations.

Wayfinding: the effects of large displays and 3-D perception.

Large displays and stereopsis have been shown to improve performance in several virtual navigation tasks. In the present research, we sought to determine whether wayfinding could benefit from these factors. Participants were tested in a virtual town. There were three viewing conditions: a desktop, a large screen, and a large screen on which the virtual environment was viewed in three dimensions (3-D) using polarized glasses. Participants explored the town and had to remember the location of several landmarks. Their memory of the layout of the town was tested by asking them to navigate from one landmark to another, taking the shortest route possible. All groups performed equally well in terms of the distance traveled to target locations. From this result, we concluded that large displays and 3-D perception do not significantly contribute to wayfinding. Thus, experimental paradigms and training programs that utilize wayfinding are as valuable when administered on standard desktops as on more sophisticated and costly equipment and do not induce simulator sickness as large displays tend to do.