Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 Jun 2.
Published in final edited form as: J Biosci. 2016 Sep;41(3):419–426. doi: 10.1007/s12038-016-9631-z

Microstructural abnormalities of uncinate fasciculus as a function of impaired cognition in schizophrenia: A DTI study

Sadhana Singh 1, Kavita Singh 1, Richa Trivedi 1, Satnam Goyal 2, Prabhjot Kaur 1, Namita Singh 1, Triptish Bhatia 2, Smita N Deshpande 2, Subash Khushu 1,*
PMCID: PMC5454024  NIHMSID: NIHMS862004  PMID: 27581933

Abstract

Neuropsychological studies have reported that attention, memory, language, motor and emotion processing are impaired in schizophrenia. It is known that schizophrenia involves structural alterations in the white matter of brain that contribute to the pathophysiology of the disorder. Uncinate fasciculus (UNC), a bundle of white matter fibres, plays an important role in the pathology of this disorder and involved in cognitive functions such as memory, language and emotion processing. Therefore, the present study aimed to investigate microstructural changes in UNC fibre in schizophrenia patients relative to controls and its correlation with neuropsychological scores.

Diffusion tensor imaging (DTI) and Hindi version of Penn Computerised Neuropsychological Battery test was performed in 14 schizophrenia patients and 14 controls. DTI measures [fractional anisotropy (FA) and mean diffusivity (MD)] from UNC fibre were calculated and a comparison was made between patients and controls. Pearson’s correlation was performed between neuropsychological scores and DTI measures. Schizophrenia patients showed significantly reduced FA values in UNC fibre compared to controls. In schizophrenia patients, a positive correlation of attention, spatial memory, sensorimotor dexterity and emotion with FA was observed. These findings suggest that microstructural changes in UNC fibre may contribute to underlying dysfunction in the cognitive functions associated with schizophrenia.

Keywords: Diffusion tensor imaging, fractional anisotropy, mean diffusivity, schizophrenia, uncinate fasciculus

1. Introduction

Schizophrenia is a psychotic disorder which is associated with a wide spectrum of disturbances, including social, cognitive and emotional dysfunction. Several neuropsychological studies have shown deficits in working memory, verbal memory, language, motor and executive functions in schizophrenia (Green 1996; Penn et al. 1997; Rund and Borg 1999; Goldberg and Green 2002). These impairments in cognitive domains reflect the abnormal functioning of the brain in schizophrenia patients.

It is evident that schizophrenia involves structural alterations in the white matter of the brain that contributes to the pathophysiology of the disorder (Davis et al. 2003). Various neuroimaging studies have shown the structure–function relationship in a large number of brain regions which are affected in schizophrenia (Niznikiewicz et al. 2003; Waddington 2007). Literature has suggested that a disruption in connectivity between different brain regions, especially between the frontal and temporal lobes, may partly explain some of the primary symptoms of schizophrenia (Friston and Frith 1995; Deakin and Simpson 1997; Kubicki et al. 2002). The frontal and temporal lobes are connected via the uncinate fasciculus (UNC) which is considered as the major fibre tract (Ungerleider et al. 1989; Catani and Thiebaut de Schotten 2008). UNC plays a putative role in episodic memory, language, semantic retrieval and social emotional processing (Von Der Heide et al. 2013). It is also associated with higher level object perception and object memory (Murray et al. 2005).

Magnetic resonance imaging (MRI)-based diffusion tensor imaging (DTI) is a non-invasive method which have the ability to quantify the integrity of white matter fibre tracts that functionally connect different brain networks. DTI measures the strength and direction of water diffusivity in brain tissue to estimate the microstructural changes of fibre tracts in various pathological conditions using DTI indices [fractional anisotropy (FA), and mean diffusivity (MD)]. FA measures the amount of coherence of water diffusion and putatively reflects the amount of myelination in axonal bundles or the coherence of fibre tracts whereas MD measure overall displacement of diffusion in a particular region or voxel. Several studies have measured the FA values in the UNC of schizophrenia using DTI (Kubicki et al. 2002; Highley et al. 2002; Burns et al. 2003; Jones et al. 2006; Price et al. 2008; Voineskos et al. 2010; Kitis et al. 2012). Most of these studies reported decreased FA values in schizophrenia patients as compared with healthy controls (Burns et al. 2003; Price et al. 2008; Voineskos et al. 2010; Kitis et al. 2012) whereas few of them showed increased or no change in FA values in the schizophrenia patients (Kubicki et al. 2002; Highley et al. 2002; Jones et al. 2006).

In the present study, we performed DTI and neuropsychological test using Penn Computerised Neuropsychological Battery (CNB) in 14 schizophrenia patients and 14 healthy subjects in order to measure the integrity of fibres within the uncinate fasciculus (UNC), the most prominent of all white matter fibre tracts connecting the frontal and temporal lobes, and its correlation with neuropsychological scores.

2. Methods

The study was conducted at the NMR Research Centre, Institute of Nuclear Medicine and Allied Sciences (INMAS), Delhi, India and was approved by the Institutional ethics committee of Post Graduate Institute of Medical Education and Research (PGIMER), Dr. Ram Manohar Lohia Hospital (RMLH), New Delhi, India, and the Institutional Review Board, INMAS, Delhi, India. After complete description of the study to the participants, written informed consent was obtained from all the participants.

2.1 Subjects

A total of 14 schizophrenia patients and 14 healthy controls underwent MRI scans (table 1). Schizophrenia subjects were recruited from outpatients at Department of Psychiatry, PGIMER- RMLH, New Delhi.

Table 1.

Demographic and clinical characteristics of subjects

Schizophrenic patients
(N = 14)
(mean±SD)		 Healthy controls
(N= 14)
(mean±SD)
Age (years) 34.14±7.60 32.36±5.57
Gender (Male/Female) 8/6 6/8
Years of education 9.7 ± 3.8 11.3 ± 3.3
Duration of Illness (in weeks) 451.1 ± 318.8 NA
SANS total score 11.61 ± 5.9
SAPS total score 7.96 ± 4.3
Antipsychotic equivalent dosage of CPZ, mg/day 507.14 ± 362.9
Duration of antipsychotic drug taken (in weeks) 451.1 ± 318.8
Open in a new tab

SANS: Scale for the Assessment of Negative Symptoms; SAPS: Scale for the Assessment of Positive Symptoms; CPZ: chlorpromazine.

We included patients aged between 25–45 years with a DSM-IV diagnosis of schizophrenia, interviewed using the Hindi version of the Diagnostic Interview for Genetic Studies (DIGS) (Nurnberger et al. 1994; Deshpande et al. 1998) after diagnostic review with a board certified psychiatrist. Patients with a history of alcohol/illicit substances or individuals with any neurological disorders that interfered with the diagnosis or cognitive evaluations were excluded. All patients were on antipsychotic medications at the timing of MRI acquisition.

All control subjects chosen for the study were recruited from the local community. During the entire study, none of the subjects displayed any clinical evidence of stroke, head injury, cardiovascular diseases, history of smoking, alcohol or drug dependence and psychiatric disorders. Both patients and controls showed no neurological abnormalities on conventional MRI scans.

2.2 Neuropsychological assessment

Neurocognitive functions of both controls and patients were assessed with a Hindi version of Penn Computerised Neuropsychological Battery (CNB) (Gur et al. 2001). The CNB includes neurocognitive domains known to be impaired among individual with schizophrenia (Sachs et al. 2004). The CNB, developed in the Brain Behaviour Lab at Penn, is a series of computerized tests that measure the accuracy and speed of performance in major domains of cognition, including social-cognition (Gur et al. 2001). Data are acquired in an automated fashion, reducing observer bias. It is also possible to measure both accuracy and response times. The construct validity of the Penn CNB has been established and it has been used extensively (Gur et al. 2001). The battery assesses the following neurocognitive domains: abstraction and mental flexibility (ABF), attention (ATT), working memory (WM), verbal memory (VMEM), face memory (FMEM), spatial memory (SPA), language (LAN), sensorimotor dexterity (SM), emotion processing (EMO). The verbal domains are available only in English. As many Indians did not speak English, the verbal domains were excluded. For each domain, three summary functions are calculated: (a) accuracy (number of correct responses) (b) speed (median reaction time for correct responses) and (c) efficiency (which reflects both accuracy and speed). The battery was administered in a fixed order using a web browser interface. Participants need not be computer literate to complete the Penn CNB, as the evaluations are straight forward. In addition, each participant is required to complete a practice session designed for familiarisation with the computer mouse and the video monitor.

2.3 Imaging protocol

Patients and controls underwent conventional MRI and DTI. Imaging was performed on a 3 tesla whole-body MRI system (Magnetom Skyra, Siemens, Germany) using a 16-channel head coil with 45 mT/m actively shielded gradient system. The conventional MR Imaging was performed to rule out any structural abnormality using axial T2-weighted turbo spin echo sequence with repetition time (TR)/echo time (TE)/number of excitations (NEX) =5600ms/100 ms/1 and T1-weighted turbo inversion recovery sequence with TR/TE/NEX=2000 ms/12ms/1 with other parameters: field of view=220×220 mm2, slice thickness = 4 mm with no interslice gap and number of slices=25. DTI data were acquired using a single-shot echo-planar spin-echo (SE) sequence with ramp sampling. Diffusion-weighted acquisition parameters were: b-factor = 0 and 1000 s/mm2, slice thickness=3 mm with no interslice space, number of slices=45, FOV=230 mm×230 mm, TR=8800 msec, TE=95 ms, NEX=2 and number of gradient encoding directions = 30.

2.4 Diffusion tensor tractography and data quantitation

Segmentation of white matter structures

Segmentation of white matter structures followed by diffusion tensor tractography (DTT) was performed using in-house developed JAVA based software (Rathore et al. 2011).

The purpose of segmentation of white matter structure was to facilitate the selection of regions of interest (ROIs) for DTT. The key idea of this method is to do a segmentation of the principal eigenvector (e1) field into stable voxels having a minimal e1 variation (curvature). Thus, a voxel P (i, j, k) is a member of the stable fibre mass (SFM), if there is a neighboring voxel Q (x, y, z) such that the principal eigenvectors e1’s at P and Q point out to each other. Mathematically, it translates to the relation G (F (P)) =P, where F (P) =ROUND (P + e1(P) + 0.5u), u = (1, 1, 1), and G (Q) = ROUND (Q − e1(Q) + 0.5u), function ROUND standing for component wise ‘integral part’ operation.

The method first generates SFM and then segments volume by colouring voxel P according to the values the components (l, m, n) of the vector joining P and Q take: (±1,0,0) - red, (0,±1,0) - green, (0,0,±1) - blue, (±1,±1,0) - yellow, (0,±1,±1) - cyan, (±1,0,±1) –magenta, (±1,±1,±1) -white. A non-stable voxel is coloured grey and a voxel with FA < 0.15 remains black. Typical segmented axial, sagittal and coronal SFM coloured maps are generated, using which this method narrows down the ROIs selection for the standard tractography to pointing out to a colour segment inside a broader ROI (Rathore et al. 2011) (figure 1).

Figure 1.

Figure 1

Open in a new tab

Flow chart explaining the algorithm used for fibre tracking, ROI: Region of interest.

Diffusion tensor tractography

Fibre assignment by continuous tracking algorithm (Mori et al. 1999) was used for reconstruction of fibres. An in-house-developed JAVA-based software (Rathore et al. 2011) was used to generate and quantify uncinate fasciculus (UNC) fibre tract. DTI measures were calculated for the entire fibre. The FA threshold of 0.15 was used for fibre tracking.

2.5 Statistical analysis

Multivariate Analysis of Variance (MANOVA) using General Linear Model was performed to measure the changes in neuropsychological scores between controls and schizophrenia patients. Student’s independent t-test was performed to determine the changes in FA/MD values in UNC fibre tracts between controls and schizophrenia patients. In schizophrenia patients as well as healthy controls, Pearson’s correlation coefficient was computed to study the correlation between the white mater tract specific DTI measures and neuropsychological scores. A p-value ≤0.05 was considered to be statistically significant. All statistical analysis was conducted using SPSS 16.0 for Windows (SPSS Inc, Chicago, IL).

3. Results

3.1 Neuropsychological scores

Table 2 shows neuropsychological test summary scores for the control and patient groups for each of the broad cognitive domains sampled: abstraction and mental flexibility (ABF), attention (ATT), working memory (WM), face memory (FMEM), spatial memory (SPA), sensorimotor dexterity (SM), emotion processing (EMO). Patients with schizophrenia scored significantly lower than control subjects across neuropsychological summary measures.

Table 2.

A summary of group means and SDs of the neuropsychological scores in schizophrenia patients and controls subjects which showed significant differences

Domains Controls Patients p-Value
Accuracy
Abstract and mental flexibility −0.08 ± 0.87 −1.87 ± 0.62 0.001
Attention   0.37 ± 0.27 −1.10 ± 1.44 0.001
Face memory   0.76 ± 1.42 −0.91 ± 0.93 0.001
Spatial memory   0.97 ± 1.03 −0.68 ± 0.51 0.001
Working memory   0.53 ± 0.67 −1.55 ± 1.35 0.001
Spatial ability   0.39 ± 0.56 −0.36 ± 0.53 0.006
Sensorimotor   0.70 ± 0.21 −0.38 ± 1.47 0.011
Emotion   0.05 ± 0.92 −0.75 ± 1.03 0.039
Speed
Abstract and mental flexibility − 0.25 ± 0.88 −1.51 ± 1.62 0.017
Attention − 0.12 ± 0.80 −1.12 ± 1.48 0.035
Face memory   0.69 ± 1.72 −1.04 ± 2.61 0.049
Spatial memory   0.69 ± 0.56 −0.79 ± 2.18 0.020
Working memory   0.18 ± 0.74 −0.85 ± 1.32 0.014
Spatial ability   0.15 ± 0.88 −2.78 ± 3.60 0.008
Sensorimotor   0.95 ± 0.64 −0.03 ± 1.16 0.011
Emotion   0.87 ± 0.77 −1.77 ± 2.81 0.002
Efficiency
Abstract and mental flexibility − 0.22 ±0.89 −2.08 ± 0.68 0.001
Attention   0.37 ± 0.43 −1.75 ± 2.05 0.001
Face memory   1.05 ± 1.67 −0.95 ± 0.97 0.001
Spatial memory   1.15 ± 1.13 −0.75 ± 0.63 0.001
Working memory   0.52 ± 0.67 −1.49 ± 1.23 0.001
Spatial ability   0.09 ± 0.82 −1.13 ± 0.75 0.003
Sensorimotor   0.94 ± 0.46 −0.48 ± 1.70 0.006
Emotion   0.44 ± 1.15 −0.81 ± 1.22 0.010
Open in a new tab

3.2 DTI measures

Table 3 shows mean ± SD of FA and MD values in UNC fibre tract in both patients and controls. The result revealed significantly reduced FA values in both left and right UNC fibre tracts in the patient group (RUNC; 0.31 ± 0.03, LUNC; 0.32 ± 0.03) as compared to control subjects (RUNC; 0.35 ± 0.05, LUNC; 0.35 ± 0.05) (RUNC; p=0.005, LUNC; p=0.031) (figure 2).

Table 3.

A summary of group means and SDs of the fractional anisotropy (FA) and Mean diffusivity (MD) values from the UNC white matter tracts of brain from the controls and schizophrenia patients (N = 14)

DTI Indices Fibre bundles Controls Schizophrenics p-Value
FA RUNC 0.35 ± 0.05 0.31 ± 0.03 0.005*
LUNC 0.35 ± 0.05 0.32 ± 0.03 0.031*
MD RUNC 0.88 ± 0.05 0.90 ± 0.04 0.320 
LUNC 0.87 ± 0.04 0.89 ± 0.04 0.182 
Open in a new tab

UNC, uncinate fasciculus. p-Values show the level of statistical significance.

Figure 2.

Figure 2

Open in a new tab

Projection of the UNC on the mid-sagittal plane in age-matched control and schizophrenia patient. 3D tractography image shows thinning of fibre bundle in patient (B) as compared with control (A).

Schizophrenia patients also showed increased MD values in UNC fibre tract compared to controls, though it could not achieve statistical significance.

3.3 Correlation between DTI measures and neuropsychological scores

Table 4 shows Pearson correlations for DTI measures of UNC and neuropsychological tests as indexed by the CNB. Schizophrenia patients showed positive correlation between attention, spatial memory, sensorimotor dexterity and emotion with FA values in UNC. In controls, no significant correlation was observed between any of the neuropsychological test and FA values.

Table 4.

Summary of the Pearson’s correlation coefficient (r) between neuropsychological scores and FA values of UNC fibre tracts in controls and schizophrenia patients which showed significant correlation.

Fibre Bundles Controls Patients
Accuracy vs FA
r-Value p-Value r-Value p-Value
Attention
 RUNC 0.188 0.519 0.598 0.024*
 LUNC 0.023 0.937 0.572 0.033*
Spatial memory
 RUNC −0.004 0.990 0.843 0.009*
Sensorimotor Dexterity
 RUNC 0.144 0.623 0.685 0.007*
Efficiency vs FA
Attention
 RUNC 0.273 0.346 0.574 0.032*
 LUNC 0.116 0.693 0.563 0.036*
Spatial memory
 RUNC 0.003 0.992 0.873 0.001*
Sensorimotor Dexterity
 RUNC 0.167 0.568 0.607 0.021*
Emotion
 RUNC 0.167 0.568 0.425 0.053*
Open in a new tab

p-Values shows the level of statistical significance and asterisk indicate significant correlation between groups

*

p<0.05).

MD values did not show any correlation with neuropsychological score in schizophrenia patients as well as control subjects.

4. Discussion

The present study demonstrates altered DTI indices as a result of microstructural changes in UNC fibre tracts and its correlation with neuropsychological functions in schizophrenia patients compared with healthy controls. There are some studies which have shown correlation of altered DTI indices in UNC fibre with memory function in schizophrenia (Nestor et al. 2004; Nestor et al. 2008; Szeszko et al. 2008). Besides this, our study showed correlation of altered FA in UNC fibre with attention, sensorimotor dexterity and emotion processing in schizophrenia patients as compared to healthy controls.

The UNC is a white matter fibre tract that connects the anterior temporal lobe with the medial and lateral orbitofrontal cortex (Ungerleider et al. 1989; Catani and Thiebaut de Schotten 2008). It is suggested that the information transmission properties of any white matter fibre tract can be predicted by the function of the regions that it connects. At the same time, the functions of cortical regions are determined by their pattern of white matter input and output (Passingham et al. 2002; Von Der Heide et al. 2013). By virtue of the geographic placement and connectivity of UNC, it is assumed that the UNC is associated with the limbic system and involved in emotion and episodic memory (Von Der Heide et al. 2013). Several studies have shown decreased FA values in the UNC of schizophrenia (Burns et al. 2003; Price et al. 2008; Voineskos et al. 2010; Kitis et al. 2012). In concordance with these studies, our findings also revealed significantly decreased FA values in UNC fibre in schizophrenia patients relative to healthy controls. MD values also increased in UNC fibre tract in patient group, but it was not statistically significant. The decreased FA values in UNC fibre might be due to demyelination, decreased neuronal fibre density, or directional coherence in this fibre tract.

It is suggested that disrupted uncinate fasciculus integrity in schizophrenia may be related to impaired memory and social cognition (Voineskos et al. 2010; Von Der Heide et al. 2013). There are studies which have shown correlation of DTI changes in UNC fibre with memory functions in schizophrenia patients (Nestor et al. 2004; Nestor et al. 2008; Szeszko et al. 2008). Szeszko et al. (2008) have reported that lower FA in UNC correlated significantly with worse memory functioning in patients with recent onset schizophrenia. Similarly, Nestor et al. (2008) showed lower FA in UNC is associated with worse declarative-episodic verbal memory in schizophrenia patients. In line with these studies, we also found a positive correlation between reduced FA values in UNC and memory functions.

In addition to correlation of altered FA values with memory functions, we also found a significant positive correlation between FA values in UNC fibre and attention, sensorimotor dexterity and emotion processing in schizophrenia patients. Healthy controls did not show any significant relationship between FA values and neuropsychological scores. The uncinate fasciculus (UNC) fibre tract connects the frontal and temporal lobes (Ungerleider et al. 1989; Kubicki et al. 2002; Catani and Thiebaut de Schotten 2008), that are involved in various cognitive functions such as attention, language, motor control and emotion processing (Miotto et al. 1996; Semendeferi et al. 1997; Patterson et al. 2007; Olson et al. 2007). These cognitive functions such as attention, motor function and emotion processing, are known to be impaired in schizophrenia (Gur et al. 2001; Goldberg and Green 2002). Our findings suggest that microstructural alteration in UNC fibre may contribute to underlying dysfunction associated with these cognitive functions in schizophrenia patients.

There are some limitations in the present study. Our sample size is relatively small; a greater number of subjects would yield more statistically significant results. Secondly, all the patients were on antipsychotic medications. Inclusion of a third group consisting of first-episode patients without medical history will be helpful to determine the potential effect of pharmacological treatment in schizophrenia. Although the effect of age on FA/MD values were reported in the literature, but we did not find any significant correlation between age and FA/MD values in both patient and control group.

5. Conclusion

In conclusion, our study reveals correlation of reduced FA values in UNC fibre with attention, sensorimotor dexterity and emotion in addition to memory in schizophrenia patients. These findings suggest that microstructural changes in UNC fibre may contribute to underlying dysfunction in the cognitive functions associated with schizophrenia.

Acknowledgments

This work was supported by DRDO R&D Project No. INM-311(4.1), and funded in part by grant from the Fogarty International Centre, NIH The Impact of Yoga Supplementation on Cognitive Function Among Indian outpatients Grant #1R01TW008289 to TB. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding agencies.

References

  1. Burns J, Job D, Bastin ME, Whalley H, Macgillivray T, Johnstone EC, Lawrie SM. Structural disconnectivity in schizophrenia: a diffusion tensormagnetic resonance imaging study. Br J Psychiatry. 2003;182:439–443. [PubMed] [Google Scholar]
  2. Catani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 2008;44:1105–1132. doi: 10.1016/j.cortex.2008.05.004. [DOI] [PubMed] [Google Scholar]
  3. Davis KL, Stewart DG, Friedman JI, Buchsbaum M, Harvey PD, Hof PR, Buxbaum J, Haroutunian V. White matter changes in schizophrenia: evidence for myelin-related dysfunction. Arch Gen Psychiatry. 2003;60:443–456. doi: 10.1001/archpsyc.60.5.443. [DOI] [PubMed] [Google Scholar]
  4. Deakin JF, Simpson MD. A two-process theory of schizophrenia: evidence from studies in post-mortem brain. J Psychiatr Res. 1997;31:277–295. doi: 10.1016/s0022-3956(96)00042-8. [DOI] [PubMed] [Google Scholar]
  5. Deshpande SN, Mathur MN, Das SK, Bhatia T, Sharma S, Nimgaonkar VL. A Hindi version of the diagnostic interview for genetic studies. Schizophr Bull. 1998;24:489–493. doi: 10.1093/oxfordjournals.schbul.a033343. [DOI] [PubMed] [Google Scholar]
  6. Friston KJ, Frith CD. Schizophrenia: a disconnection syndrome? Clin Neurosci. 1995;3:89–97. [PubMed] [Google Scholar]
  7. Goldberg TE, Green MF. Neurocognitive functioning in patients with schizophrenia: an overview. In: Davis KL, Charney D, Coyle JT, Nemeroff C, editors. Neuropsychopharmacology - fifth generation of progress. 2002. pp. 657–669. [Google Scholar]
  8. Green MF. What are the functional consequences of neurocognitive deficits in schizophrenia. Am J Psychiatry. 1996;153:321–330. doi: 10.1176/ajp.153.3.321. [DOI] [PubMed] [Google Scholar]
  9. Gur RC, Ragland JD, Moberg PJ, Bilker WB, Kohler C, Siegel SJ, Gur RE. Computerized neurocognitive scanning: II. The profile of schizophrenia. Neuropsychopharmacology. 2001;25:777–788. doi: 10.1016/S0893-133X(01)00279-2. [DOI] [PubMed] [Google Scholar]
  10. Highley JR, Walker MA, Esiri MM, Crow TJ, Harrison PJ. Asymmetry of the uncinate fasciculus: a post-mortem study of normal subjects and patients with schizophrenia. Br Cereb Cortex. 2002;12:1218–1224. doi: 10.1093/cercor/12.11.1218. [DOI] [PubMed] [Google Scholar]
  11. Jones DK, Catani M, Pierpaoli C, Reeves SJ, Shergill SS, O’Sullivan M, et al. Age effects on diffusion tensor magnetic resonance imaging tractography measures of frontal cortex connections in schizophrenia. Hum Brain Mapp. 2006;27:230–238. doi: 10.1002/hbm.20179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kitis O, Ozalay O, Zengin B, Haznedaroglu D, Eker MC, Yalvac D, Oguz K, Coburn K, et al. Reduced left uncinate fasciculus fractional anisotropy in deficit schizophrenia but not in non-deficit schizophrenia. Psychiatry Clin Neurosci. 2012;66:34–43. doi: 10.1111/j.1440-1819.2011.02293.x. [DOI] [PubMed] [Google Scholar]
  13. Kubicki M, Westin CF, Maier SE, Frumin M, Nestor PG, Salisbury DF, Kikinis R, Jolesz FA, et al. Uncinate fasciculus findings in schizophrenia: a magnetic resonance diffusion tensor imaging study. Am J Psychiatry. 2002;159:813–820. doi: 10.1176/appi.ajp.159.5.813. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Miotto EC, Bullock P, Polkey CE, Morris RG. Spatial working memory and strategy formation in patients with frontal lobe excisions. Cortex. 1996;32:613–630. doi: 10.1016/s0010-9452(96)80034-7. [DOI] [PubMed] [Google Scholar]
  15. Mori S, Crain BJ, Chacko VP, van Zijl PCM. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol. 1999;45:265–269. doi: 10.1002/1531-8249(199902)45:2<265::aid-ana21>3.0.co;2-3. [DOI] [PubMed] [Google Scholar]
  16. Murray EA, Graham KS, Gaffan D. Perirhinal cortex and its neighbours in the medial temporal lobe: contributions to memory and perception. Q J Exp Psychol. 2005;58B:378–396. doi: 10.1080/02724990544000077. [DOI] [PubMed] [Google Scholar]
  17. Nestor PG, Kubicki M, Gurrera RJ, Niznikiewicz M, Frumin M, McCarley RW, Shenton ME. Neuropsychological correlates of diffusion tensor imaging in schizophrenia. Neuropsychology. 2004;18:629–637. doi: 10.1037/0894-4105.18.4.629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Nestor PG, Kubicki M, Niznikiewicz M, Gurrera RJ, McCarley RW, Shenton ME. Neuropsychological disturbances in schizophrenia: a diffusion tensor imaging study. Neuropsychology. 2008;22:246–254. doi: 10.1037/0894-4105.22.2.246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Niznikiewicz MA, Kubicki M, Shenton ME. Recent structural and functional imaging findings in schizophrenia. Curr Opin Psychiatry. 2003;16:123–147. [Google Scholar]
  20. Nurnberger JI, Jr, Blehar MC, Kaufmann CA. Diagnostic interview for genetic studies. Rationale, unique features and training. NIHM Genetics Initiative. Arch Gen Psychiatry. 1994;51:849–859. doi: 10.1001/archpsyc.1994.03950110009002. [DOI] [PubMed] [Google Scholar]
  21. Olson IR, Plotzker A, Ezzyat Y. The enigmatic temporal pole: a review of findings on social and emotional processing. Brain. 2007;130:1718–1731. doi: 10.1093/brain/awm052. [DOI] [PubMed] [Google Scholar]
  22. Patterson K, Nestor PJ, Rogers TT. Where do you know what you know? The representation of semantic knowledge in the human brain. Nat Rev Neurosci. 2007;8:976–987. doi: 10.1038/nrn2277. [DOI] [PubMed] [Google Scholar]
  23. Penn DL, Spaulding W, Reed D, Sullivan M, Mueser KT, Hope DA. Cognition and social functioning in schizophrenia. Psychiatry. 1997;60:281–291. doi: 10.1080/00332747.1997.11024806. [DOI] [PubMed] [Google Scholar]
  24. Price G, Cercigani M, Parker GJM, Altmann DR, Barnes TRE, Barker GJ, Joyce EM, Ron MA. White matter tracts in first-episode psychosis: a DTI tractography study of the uncinate fasciculus. Neuroimage. 2008;39:949–955. doi: 10.1016/j.neuroimage.2007.09.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Rathore RK, Gupta RK, Agarwal S, Trivedi R, Tripathi RP, Awasthi R. Principal eigenvector field segmentation for reproducible diffusion tensor tractography of white matter structures. Magn Reson Imaging. 2011;29:1088–1100. doi: 10.1016/j.mri.2011.04.014. [DOI] [PubMed] [Google Scholar]
  26. Rund BR, Borg NE. Cognitive deficits and cognitive training in schizophrenic patients: a review. Acta Psychiatry Scand. 1999;100:85–95. doi: 10.1111/j.1600-0447.1999.tb10829.x. [DOI] [PubMed] [Google Scholar]
  27. Sachs G, Steger-Wuchse D, Kryspin-Exner I, Gur RC, Katsching H. Facial recognition deficits and cognition in schizophrenia. Schizophr Res. 2004;68:27–35. doi: 10.1016/S0920-9964(03)00131-2. [DOI] [PubMed] [Google Scholar]
  28. Semendeferi K, Damasio H, Frank R, Van Hoesen GW. The evolution of the frontal lobes: a volumetric analysis based on three-dimensional reconstructions of magnetic resonance scans of human and ape brains. J Hum Evol. 1997;32:375–388. doi: 10.1006/jhev.1996.0099. [DOI] [PubMed] [Google Scholar]
  29. Szeszko PR, Robinson DG, Ashtari M, Vogel J, Betensky J, Sevy S, Ardekani BA, Lencz T, et al. Clinical and neuropsychological correlates of white matter abnormalities in recent onset schizophrenia. Neuropsychopharmacology. 2008;33:976–984. doi: 10.1038/sj.npp.1301480. [DOI] [PubMed] [Google Scholar]
  30. Ungerleider LG, Gaffan D, Pelak VS. Projections from inferior temporal cortex to prefrontal cortex via the uncinate fascicle in rhesus monkeys. Exp Brain Res. 1989;76:473–484. doi: 10.1007/BF00248903. [DOI] [PubMed] [Google Scholar]
  31. Voineskos AN, Lobaugh NJ, Bouix S, Rajji TK, Dielle Miranda D, et al. Diffusion tensor tractography findings in schizophrenia across the adult lifespan. Brain. 2010;133:1494–1504. doi: 10.1093/brain/awq040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Von Der Heide RJ, Skipper LM, Klobusicky E, Olson IR. Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain. 2013;136:1692–1707. doi: 10.1093/brain/awt094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Waddington JL. Neuroimaging and other neurobiological indices in schizophrenia: relationship to measurement of functional outcome. Br J Psychiatry. 2007;191:s52–s57. doi: 10.1192/bjp.191.50.s52. [DOI] [PubMed] [Google Scholar]

RESOURCES