Pedunculopontine nucleus deep brain stimulation produces sustained improvement in primary progressive freezing of gait

Objective: To assess the efficacy of bilateral pedunculopontine nucleus (PPN) deep brain stimulation (DBS) as a treatment for primary progressive freezing of gait (PPFG). ------ ----- Methods: A patient with PPFG underwent bilateral PPN-DBS and was followed clinically for over 14 months. ------ ----- Results: The PPFG patient exhibited a robust improvement in gait and posture following PPN-DBS. When PPN stimulation was deactivated, postural stability and gait skills declined to pre-DBS levels, and fluoro-2-deoxy-d-glucose positron emission tomography revealed hypoactive cerebellar and brainstem regions, which significantly normalised when PPN stimulation was reactivated. ------ ----- Conclusions: This case demonstrates that the advantages of PPN-DBS may not be limited to addressing freezing of gait (FOG) in idiopathic Parkinson's disease. The PPN may also be an effective DBS target to address other forms of central gait failure.

Nutritional status in Parkinson's disease patients undergoing deep-brain stimulation surgery : a pilot study

People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. Poorer outcomes are associated with higher amounts of weight loss (>5%) and lower levels of fat free mass. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=11 out of 16). The Scale for Outcomes of Parkinson’s disease –Autonomic (SCOPA-AUT), Modified Constipation Assessment Scale (MCAS), and a 3-day food diary were mailed to participants’ homes for completion prior to hospital admission. During admission, the Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed. Mean(±s.d.) PD duration from diagnosis and time since occurrence of PD symptoms was 9.0(±8.0) and 12(±8.8) years, respectively. Five participants reported unintentional weight loss (average loss of 15.6%). PD duration but not years since symptom onset significantly predicted PG-SGA scores (β=4.2, t(8)=2.7, p<.05). Both were positively correlated with PG-SGA score (r = .667, r=.587). On average, participants classified as well-nourished (SGA-A) (n=4) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass (FFMI) indices when compared to malnourished participants (SGA-B) (n=7). They also reported fewer non-motor symptoms on the SCOPA-AUT and MCAS. Three participants had previously received dietetic advice but not in relation to PD. These findings demonstrate that malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and a high prevalence of malnutrition.

Nutritional status in Parkinson’s disease patients undergoing deep brain stimulation surgery : a pilot study

Objectives: People with Parkinson’s disease (PD) are at higher risk of malnutrition due to PD symptoms and pharmacotherapy side effects. When pharmacotherapy is no longer effective for symptom control, deep-brain stimulation (DBS) surgery may be considered. The aim of this study was to assess the nutritional status of people with PD who may be at higher risk of malnutrition related to unsatisfactory symptom management with optimised medical therapy. Design: This was an observational study using a convenience sample. Setting: Participants were seen during their hospital admission for their deep brain stimulation surgery. Participants: People with PD scheduled for DBS surgery were recruited from a Brisbane neurological clinic (n=15). Measurements: The Patient-Generated Subjective Global Assessment (PG-SGA), weight, height and body composition were assessed to determine nutritional status. Results: Six participants (40%) were classified as moderately malnourished (SGA-B). Eight participants (53%) reported previous unintentional weight loss (average loss of 13.3%). On average, participants classified as well-nourished (SGA-A) were younger, had shorter disease durations, lower PG-SGA scores, higher body mass (BMI) and fat free mass indices (FFMI) when compared to malnourished participants (SGA-B). Five participants had previously received dietetic advice but only one in relation to unintentional weight loss. Conclusion: Malnutrition remains unrecognised and untreated in this group despite unintentional weight loss and presence of nutrition impact symptoms. Improving nutritional status prior to surgery may improve surgical outcomes.

A spatiotemporal analysis of gait freezing and the impact of pedunculopontine nucleus stimulation

Gait freezing is an episodic arrest of locomotion due to an inability to take normal steps. Pedunculopontine nucleus stimulation is an emerging therapy proposed to improve gait freezing, even where refractory to medication. However, the efficacy and precise effects of pedunculopontine nucleus stimulation on Parkinsonian gait disturbance are not established. The clinical application of this new therapy is controversial and it is unknown if bilateral stimulation is more effective than unilateral. Here, in a double-blinded study using objective spatiotemporal gait analysis, we assessed the impact of unilateral and bilateral pedunculopontine nucleus stimulation on triggered episodes of gait freezing and on background deficits of unconstrained gait in Parkinson’s disease. Under experimental conditions, while OFF medication, Parkinsonian patients with severe gait freezing implanted with pedunculopontine nucleus stimulators below the pontomesencephalic junction were assessed during three conditions; off stimulation, unilateral stimulation and bilateral stimulation. Results were compared to Parkinsonian patients without gait freezing matched for disease severity and healthy controls. Pedunculopontine nucleus stimulation improved objective measures of gait freezing, with bilateral stimulation more effective than unilateral. During unconstrained walking, Parkinsonian patients who experience gait freezing had reduced step length and increased step length variability compared to patients without gait freezing; however, these deficits were unchanged by pedunculopontine nucleus stimulation. Chronic pedunculopontine nucleus stimulation improved Freezing of Gait Questionnaire scores, reflecting a reduction of the freezing encountered in patients’ usual environments and medication states. This study provides objective, double-blinded evidence that in a specific subgroup of Parkinsonian patients, stimulation of a caudal pedunculopontine nucleus region selectively improves gait freezing but not background deficits in step length. Bilateral stimulation was more effective than unilateral.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Malnutrition in Parkinson's disease and an individualised approach to its management

It has been reported that poor nutritional status, in the form of weight loss and resulting body mass index (BMI) changes, is an issue in people with Parkinson's disease (PWP). The symptoms resulting from Parkinson's disease (PD) and the side effects of PD medication have been implicated in the aetiology of nutritional decline. However, the evidence on which these claims are based is, on one hand, contradictory, and on the other, restricted primarily to otherwise healthy PWP. Despite the claims that PWP suffer from poor nutritional status, evidence is lacking to inform nutrition-related care for the management of malnutrition in PWP. The aims of this thesis were to better quantify the extent of poor nutritional status in PWP, determine the important factors differentiating the well-nourished from the malnourished and evaluate the effectiveness of an individualised nutrition intervention on nutritional status. Phase DBS: Nutritional status in people with Parkinson's disease scheduled for deep-brain stimulation surgery The pre-operative rate of malnutrition in a convenience sample of people with Parkinson's disease (PWP) scheduled for deep-brain stimulation (DBS) surgery was determined. Poorly controlled PD symptoms may result in a higher risk of malnutrition in this sub-group of PWP. Fifteen patients (11 male, median age 68.0 (42.0 – 78.0) years, median PD duration 6.75 (0.5 – 24.0) years) participated and data were collected during hospital admission for the DBS surgery. The scored PG-SGA was used to assess nutritional status, anthropometric measures (weight, height, mid-arm circumference, waist circumference, body mass index (BMI)) were taken, and body composition was measured using bioelectrical impedance spectroscopy (BIS). Six (40%) of the participants were malnourished (SGA-B) while 53% reported significant weight loss following diagnosis. BMI was significantly different between SGA-A and SGA-B (25.6 vs 23.0kg/m 2, p<.05). There were no differences in any other variables, including PG-SGA score and the presence of non-motor symptoms. The conclusion was that malnutrition in this group is higher than that in other studies reporting malnutrition in PWP, and it is under-recognised. As poorer surgical outcomes are associated with poorer pre-operative nutritional status in other surgeries, it might be beneficial to identify patients at nutritional risk prior to surgery so that appropriate nutrition interventions can be implemented. Phase I: Nutritional status in community-dwelling adults with Parkinson's disease The rate of malnutrition in community-dwelling adults (>18 years) with Parkinson's disease was determined. One hundred twenty-five PWP (74 male, median age 70.0 (35.0 – 92.0) years, median PD duration 6.0 (0.0 – 31.0) years) participated. The scored PG-SGA was used to assess nutritional status, anthropometric measures (weight, height, mid-arm circumference (MAC), calf circumference, waist circumference, body mass index (BMI)) were taken. Nineteen (15%) of the participants were malnourished (SGA-B). All anthropometric indices were significantly different between SGA-A and SGA-B (BMI 25.9 vs 20.0kg/m2; MAC 29.1 – 25.5cm; waist circumference 95.5 vs 82.5cm; calf circumference 36.5 vs 32.5cm; all p<.05). The PG-SGA score was also significantly lower in the malnourished (2 vs 8, p<.05). The nutrition impact symptoms which differentiated between well-nourished and malnourished were no appetite, constipation, diarrhoea, problems swallowing and feel full quickly. This study concluded that malnutrition in community-dwelling PWP is higher than that documented in community-dwelling elderly (2 – 11%), yet is likely to be under-recognised. Nutrition impact symptoms play a role in reduced intake. Appropriate screening and referral processes should be established for early detection of those at risk. Phase I: Nutrition assessment tools in people with Parkinson's disease There are a number of validated and reliable nutrition screening and assessment tools available for use. None of these tools have been evaluated in PWP. In the sample described above, the use of the World Health Organisation (WHO) cut-off (≤18.5kg/m2), age-specific BMI cut-offs (≤18.5kg/m2 for under 65 years, ≤23.5kg/m2 for 65 years and older) and the revised Mini-Nutritional Assessment short form (MNA-SF) were evaluated as nutrition screening tools. The PG-SGA (including the SGA classification) and the MNA full form were evaluated as nutrition assessment tools using the SGA classification as the gold standard. For screening, the MNA-SF performed the best with sensitivity (Sn) of 94.7% and specificity (Sp) of 78.3%. For assessment, the PG-SGA with a cut-off score of 4 (Sn 100%, Sp 69.8%) performed better than the MNA (Sn 84.2%, Sp 87.7%). As the MNA has been recommended more for use as a nutrition screening tool, the MNA-SF might be more appropriate and take less time to complete. The PG-SGA might be useful to inform and monitor nutrition interventions. Phase I: Predictors of poor nutritional status in people with Parkinson's disease A number of assessments were conducted as part of the Phase I research, including those for the severity of PD motor symptoms, cognitive function, depression, anxiety, non-motor symptoms, constipation, freezing of gait and the ability to carry out activities of daily living. A higher score in all of these assessments indicates greater impairment. In addition, information about medical conditions, medications, age, age at PD diagnosis and living situation was collected. These were compared between those classified as SGA-A and as SGA-B. Regression analysis was used to identify which factors were predictive of malnutrition (SGA-B). Differences between the groups included disease severity (4% more severe SGA-A vs 21% SGA-B, p<.05), activities of daily living score (13 SGA-A vs 18 SGA-B, p<.05), depressive symptom score (8 SGA-A vs 14 SGA-B, p<.05) and gastrointestinal symptoms (4 SGA-A vs 6 SGA-B, p<.05). Significant predictors of malnutrition according to SGA were age at diagnosis (OR 1.09, 95% CI 1.01 – 1.18), amount of dopaminergic medication per kg body weight (mg/kg) (OR 1.17, 95% CI 1.04 – 1.31), more severe motor symptoms (OR 1.10, 95% CI 1.02 – 1.19), less anxiety (OR 0.90, 95% CI 0.82 – 0.98) and more depressive symptoms (OR 1.23, 95% CI 1.07 – 1.41). Significant predictors of a higher PG-SGA score included living alone (β=0.14, 95% CI 0.01 – 0.26), more depressive symptoms (β=0.02, 95% CI 0.01 – 0.02) and more severe motor symptoms (OR 0.01, 95% CI 0.01 – 0.02). More severe disease is associated with malnutrition, and this may be compounded by lack of social support. Phase II: Nutrition intervention Nineteen of the people identified in Phase I as requiring nutrition support were included in Phase II, in which a nutrition intervention was conducted. Nine participants were in the standard care group (SC), which received an information sheet only, and the other 10 participants were in the intervention group (INT), which received individualised nutrition information and weekly follow-up. INT gained 2.2% of starting body weight over the 12 week intervention period resulting in significant increases in weight, BMI, mid-arm circumference and waist circumference. The SC group gained 1% of starting weight over the 12 weeks which did not result in any significant changes in anthropometric indices. Energy and protein intake (18.3kJ/kg vs 3.8kJ/kg and 0.3g/kg vs 0.15g/kg) increased in both groups. The increase in protein intake was only significant in the SC group. The changes in intake, when compared between the groups, were no different. There were no significant changes in any motor or non-motor symptoms or in "off" times or dyskinesias in either group. Aspects of quality of life improved over the 12 weeks as well, especially emotional well-being. This thesis makes a significant contribution to the evidence base for the presence of malnutrition in Parkinson's disease as well as for the identification of those who would potentially benefit from nutrition screening and assessment. The nutrition intervention demonstrated that a traditional high protein, high energy approach to the management of malnutrition resulted in improved nutritional status and anthropometric indices with no effect on the presence of Parkinson's disease symptoms and a positive effect on quality of life.

Current practice in nutrition diagnosis and intervention for the management of Parkinson's disease in Australia and Canada

Aim To document current practice by dietitians in Australia and Canada in the nutrition management of Parkinson's disease. This will help identify priority areas for review and development of practice guidelines and direct future research. Methods Current practice in the phases of the Nutrition Care Plan was captured using an online survey distributed to Dietitians Association of Australia members and Practice-Based Evidence in Nutrition subscribers through their email newsletters. The results of the diagnosis, intervention and monitoring phases are presented here. Results Eighty-four dietitians responded. There was consistency in practice for nutrition issues that are encountered in other populations, such as malnutrition and constipation. There was more variation in practice in the nutrition issues that are more specific to Parkinson's disease, such as nutrition and meal interactions with medication. A lack of awareness of emerging treatments, such as deep brain stimulation surgery, appears to exist in the responding dietitians. Conclusions The variation in practice that was present for the nutrition issues specific to Parkinson's disease may reflect the lack of quality evidence and subsequently evidence-based guidelines in these areas. Work to provide background information about treatment options and to translate current evidence for the nutrition issues that are specific to Parkinson's disease into practice recommendations should be completed.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. Rev. , 36 (2--3), 2001, 108--118. doi:10.1016/S0165-0173(01)00086-8 S. Ogawa, R. S. Menon, S. G. Kim and K. Ugurbil, On the characteristics of functional magnetic resonance imaging of the brain, Annu. Rev. Bioph. Biom. , 27 , 1998, 447--474. doi:10.1146/annurev.biophys.27.1.447 C. D. Binnie and H. Stefan, Modern electroencephalography: its role in epilepsy management, Clin. Neurophysiol. , 110 (10), 1999, 1671--1697. doi:10.1016/S1388-2457(99)00125-X J. X. Tao, A. Ray, S. Hawes-Ebersole and J. S. Ebersole, Intracranial eeg substrates of scalp eeg interictal spikes, Epilepsia , 46 (5), 2005, 669--76. doi:10.1111/j.1528-1167.2005.11404.x S. Ogawa, D. W. Tank, R. Menon, J. M. Ellermann, S. G. Kim, H. Merkle and K. Ugurbil, Intrinsic signal changes accompanying sensory stimulation: Functional brain mapping with magnetic resonance imaging, P. Natl. Acad. Sci. USA , 89 (13), 1992, 5951--5955. doi:10.1073/pnas.89.13.5951 J. Engel Jr., Report of the ilae classification core group, Epilepsia , 47 (9), 2006, 1558--1568. doi:10.1111/j.1528-1167.2006.00215.x L. Lemieux, A. Salek-Haddadi, O. Josephs, P. Allen, N. Toms, C. Scott, K. Krakow, R. Turner and D. R. Fish, Event-related fmri with simultaneous and continuous eeg: description of the method and initial case r port, NeuroImage , 14 (3), 2001, 780--7. doi:10.1006/nimg.2001.0853 P. Federico, D. F. Abbott, R. S. Briellmann, A. S. Harvey and G. D. Jackson, Functional mri of the pre-ictal state, Brain , 128 (8), 2005, 1811-7. doi:10.1093/brain/awh533 C. S. Hawco, A. P. Bagshaw, Y. Lu, F. Dubeau and J. Gotman, bold changes occur prior to epileptic spikes seen on scalp eeg, NeuroImage , 35 (4), 2007, 1450--1458. doi:10.1016/j.neuroimage.2006.12.042 F. Moeller, H. R. Siebner, S. Wolff, H. Muhle, R. Boor, O. Granert, O. Jansen, U. Stephani and M. Siniatchkin, Changes in activity of striato-thalamo-cortical network precede generalized spike wave discharges, NeuroImage , 39 (4), 2008, 1839--1849. doi:10.1016/j.neuroimage.2007.10.058 V. Osharina, E. Ponchel, A. Aarabi, R. Grebe and F. Wallois, Local haemodynamic changes preceding interictal spikes: A simultaneous electrocorticography (ecog) and near-infrared spectroscopy (nirs) analysis in rats, NeuroImage , 50 (2), 2010, 600--607. doi:10.1016/j.neuroimage.2010.01.009 R. S. Fisher, W. Boas, W. Blume, C. Elger, P. Genton, P. Lee and J. Engel, Epileptic seizures and epilepsy: Definitions proposed by the international league against epilepsy (ilae) and the international bureau for epilepsy (ibe), Epilepsia , 46 (4), 2005, 470--472. doi:10.1111/j.0013-9580.2005.66104.x H. Berger, Electroencephalogram in humans, Arch. Psychiat. Nerven. , 87 , 1929, 527--570. C. M. Michel, M. M. Murray, G. Lantz, S. Gonzalez, L. Spinelli and R. G. de Peralta, eeg source imaging, Clin. Neurophysiol. , 115 (10), 2004, 2195--2222. doi:10.1016/j.clinph.2004.06.001 P. L. Nunez and R. B. Silberstein, On the relationship of synaptic activity to macroscopic measurements: Does co-registration of eeg with fmri make sense?, Brain Topogr. , 13 (2), 2000, 79--96. doi:10.1023/A:1026683200895 S. Ogawa, T. M. Lee, A. R. Kay and D. W. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation, P. Natl. Acad. Sci. USA , 87 (24), 1990, 9868--9872. doi:10.1073/pnas.87.24.9868 J. S. Gati, R. S. Menon, K. Ugurbil and B. K. Rutt, Experimental determination of the bold field strength dependence in vessels and tissue, Magn. Reson. Med. , 38 (2), 1997, 296--302. doi:10.1002/mrm.1910380220 P. A. Bandettini, E. C. Wong, R. S. Hinks, R. S. Tikofsky and J. S. Hyde, Time course EPI of human brain function during task activation, Magn. Reson. Med. , 25 (2), 1992, 390--397. K. K. Kwong, J. W. Belliveau, D. A. Chesler, I. E. Goldberg, R. M. Weisskoff, B. P. Poncelet, D. N. Kennedy, B. E. Hoppelm, M. S. Cohen and R. Turner, Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation, P. Natl. Acad. Sci. USA , 89 (12), 1992, 5675--5679. doi:10.1073/pnas.89.12.5675 J. Frahm, K. D. Merboldt and W. Hnicke, Functional mri of human brain activation at high spatial resolution, Magn. Reson. Med. , 29 (1), 1993, 139--144. P. A. Bandettini, A. Jesmanowicz, E. C. Wong and J. S. Hyde, Processing strategies for time-course data sets in functional MRI of the human brain, Magn. Reson. Med. , 30 (2), 1993, 161--173. K. J. Friston, P. Jezzard and R. Turner, Analysis of functional MRI time-series, Hum. Brain Mapp. , 1 (2), 1994, 153--171. B. Biswal, F. Z. Yetkin, V. M. Haughton and J. S. Hyde, Functional connectivity in the motor cortex of resting human brain using echo-planar mri, Mag. Reson. Med. , 34 (4), 1995, 537--541. doi:10.1002/mrm.1910340409 K. J. Friston, J. Ashburner, C. D. Frith, J. Poline, J. D. Heather and R. S. J. Frackowiak, Spatial registration and normalization of images, Hum. Brain Mapp. , 3 (3), 1995, 165--189. K. J. Friston, S. Williams, R. Howard, R. S. Frackowiak and R. Turner, Movement-related effects in fmri time-series, Magn. Reson. Med. , 35 (3), 1996, 346--355. G. H. Glover, T. Q. Li and D. Ress, Image-based method for retrospective correction of physiological motion effects in fmri: Retroicor, Magn. Reson. Med. , 44 (1), 2000, 162--167. doi:10.1002/1522-2594(200007)44:13.0.CO;2-E K. J. Friston, O. Josephs, G. Rees and R. Turner, Nonlinear event-related responses in fmri, Magn. Reson. Med. , 39 (1), 1998, 41--52. doi:10.1002/mrm.1910390109 K. Ugurbil, L. Toth and D. Kim, How accurate is magnetic resonance imaging of brain function?, Trends Neurosci. , 26 (2), 2003, 108--114. doi:10.1016/S0166-2236(02)00039-5 D. S. Kim, I. Ronen, C. Olman, S. G. Kim, K. Ugurbil and L. J. Toth, Spatial relationship between neuronal activity and bold functional mri, NeuroImage , 21 (3), 2004, 876--885. doi:10.1016/j.neuroimage.2003.10.018 A. Connelly, G. D. Jackson, R. S. Frackowiak, J. W. Belliveau, F. Vargha-Khadem and D. G. Gadian, Functional mapping of activated human primary cortex with a clinical mr imaging system, Radiology , 188 (1), 1993, 125--130. L. Allison, Hidden Markov Models, Technical Report , School of Computer and Software Engineering, Monash University, 2000. R. J. Elliott, L. Aggoun and J.B. Moore, Hidden Markov Models: Estimation and Control, Appl. Math.-Czech. , 2004. B. Bhavnagri, Discontinuities of plane functions projected from a surface with methods for finding these , Technical Report, 2009. B. Bhavnagri, Computer Vision using Shape Spaces , Technical Report,1996, University of Adelaide. B. Bhavnagri, A method for representing shape based on an equivalence relation on polygons, Pattern Recogn. , 27 (2), 1994, 247--260. doi:10.1016/0031-3203(94)90057-4 D. F. Abbott, A. B. Waites, A. S. Harvey and G. D. Jackson, Exploring epileptic seizure onset with fmri, NeuroImage , 36(S1) (344TH-PM), 2007. M. C. Mackey and L. Glass, Oscillation and chaos in physiological control systems, Science , 197 , 1977, 287--289. S. H. Strogatz, SYNC - The Emerging Science of Spontaneous Order , Theia, New York, 2003. J. W. Kim, J. A. Roberts and P. A. Robinson, Dynamics of epileptic seizures: Evolution, spreading, and suppression, J. Theor. Biol. , 257 (4), 2009, 527--532. doi:10.1016/j.jtbi.2008.12.009 Y. Kuramoto, T. Aoyagi, I. Nishikawa, T. Chawanya T and K. Okuda, Neural network model carrying phase information with application to collective dynamics, J. Theor. Phys. , 87 (5), 1992, 1119--1126. V. B. Mountcastle, The columnar organization of the neocortex, Brain , 120 (4), 1997, 701. doi:10.1093/brain/120.4.701 F. L. Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski and D. N. Velis, Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity, Epilepsia , 44 (12), 2003, 72--83. F. H. Lopes da Silva, W. Blanes, S. N. Kalitzin, J. Parra, P. Suffczynski and D. N. Velis, Dynamical diseases of brain systems: different routes to epileptic seizures, ieee T. Bio-Med. Eng. , 50 (5), 2003, 540. L.D. Iasemidis, Epileptic seizure prediction and control, ieee T. Bio-Med. Eng. , 50 (5), 2003, 549--558. L. D. Iasemidis, D. S. Shiau, W. Chaovalitwongse, J. C. Sackellares, P. M. Pardalos, J. C. Principe, P. R. Carney, A. Prasad, B. Veeramani, and K. Tsakalis, Adaptive epileptic seizure prediction system, ieee T. Bio-Med. Eng. , 50 (5), 2003, 616--627. K. Lehnertz, F. Mormann, T. Kreuz, R.G. Andrzejak, C. Rieke, P. David and C. E. Elger, Seizure prediction by nonlinear eeg analysis, ieee Eng. Med. Biol. , 22 (1), 2003, 57--63. doi:10.1109/MEMB.2003.1191451 K. Lehnertz, R. G. Andrzejak, J. Arnhold, T. Kreuz, F. Mormann, C. Rieke, G. Widman and C. E. Elger, Nonlinear eeg analysis in epilepsy: Its possible use for interictal focus localization, seizure anticipation, and prevention, J. Clin. Neurophysiol. , 18 (3), 2001, 209. B. Litt and K. Lehnertz, Seizure prediction and the preseizure period, Curr. Opin. Neurol. , 15 (2), 2002, 173. doi:10.1097/00019052-200204000-00008 B. Litt and J. Echauz, Prediction of epileptic seizures, Lancet Neurol. , 1 (1), 2002, 22--30. doi:10.1016/S1474-4422(02)00003-0 M. M{a}kiranta, J. Ruohonen, K Suominen, J. Niinim{a}ki, E. Sonkaj{a}rvi, V. Kiviniemi, T. Sepp{a}nen, S. Alahuhta, V. J{a}ntti and O. Tervonen, {bold} signal increase preceeds eeg spike activity--a dynamic penicillin induced focal epilepsy in deep anesthesia, NeuroImage , 27 (4), 2005, 715--724. doi:10.1016/j.neuroimage.2005.05.025 K. Lehnertz, F. Mormann, H. Osterhage, A. M{u}ller, J. Prusseit, A. Chernihovskyi, M. Staniek, D. Krug, S. Bialonski and C. E. Elger, State-of-the-art of seizure prediction, J. Clin. Neurophysiol. , 24 (2), 2007, 147. doi:10.1097/WNP.0b013e3180336f16 F. Mormann, T. Kreuz, C. Rieke, R. G. Andrzejak, A. Kraskov, P. David, C. E. Elger and K. Lehnertz, On the predictability of epileptic seizures, Clin. Neurophysiol. , 116 (3), 2005, 569--587. doi:10.1016/j.clinph.2004.08.025 F. Mormann, R. G. Andrzejak, C. E. Elger and K. Lehnertz, Seizure prediction: the long and winding road, Brain , 130 (2), 2007, 314--333. doi:10.1093/brain/awl241 Z. Rogowski, I. Gath and E. Bental, On the prediction of epileptic seizures, Biol. Cybern. , 42 (1), 1981, 9--15. Y. Salant, I. Gath, O. Henriksen, Prediction of epileptic seizures from two-channel eeg, Med. Biol. Eng. Comput. , 36 (5), 1998, 549--556. doi:10.1007/BF02524422 J. Gotman and D.J. Koffler, Interictal spiking increases after seizures but does not after decrease in medication, Evoked Potential , 72 (1), 1989, 7--15. J. Gotman and M. G. Marciani, Electroencephalographic spiking activity, drug levels, and seizure occurence in epileptic patients, Ann. Neurol. , 17 (6), 1985, 59--603. A. Katz, D. A. Marks, G. McCarthy and S. S. Spencer, Does interictal spiking change prior to seizures?, Electroen. Clin. Neuro. , 79 (2), 1991, 153--156. A. Granada, R. M. Hennig, B. Ronacher, A. Kramer and H. Herzel, Phase Response Curves: Elucidating the dynamics of couples oscillators, Method Enzymol. , 454 (A), 2009, 1--27. doi:10.1016/S0076-6879(08)03801-9 doi:10.1016/S0076-6879(08)03801-9 H. Kantz and T. Schreiber, Nonlinear time series analysis , 2004, Cambridge Univ Press. M. V. L. Bennett and R. S Zukin, Electrical coupling and neuronal synchronization in the mammalian brain, Neuron , 41 (4), 2004, 495 --511. doi:10.1016/S0896-6273(04)00043-1 L.D. Iasemidis, J. Chris Sackellares, H. P. Zaveri and W. J. Williams, Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures, Brain Topogr. , 2 (3), 1990, 187--201. doi:10.1007/BF01140588 M. Le Van Quyen, J. Martinerie, V. Navarro, M. Baulac and F. J. Varela, Characterizing neurodynamic changes before seizures, J. Clin. Neurophysiol. , 18 (3), 2001, 191. J. Martinerie, C. Adam, M. Le Van Quyen, M. Baulac, S. Clemenceau, B. Renault and F. J. Varela, Epileptic seizures can be anticipated by non-linear analysis, Nat. Med. , 4 (10), 1998, 1173--1176. doi:10.1038/2667 A. Pikovsky, M. Rosenblum, J. Kurths and R. C. Hilborn, Synchronization: A universal concept in nonlinear science, Amer. J. Phys. , 70 , 2002, 655. H. R. Wilson and J. D. Cowan, Excitatory and inhibitory interactions in localized populations of model neurons, Biophys. J. , 12 (1), 1972, 1--24. D. Cumin and C. P. Unsworth, Generalising the Kuramoto model for the study of neuronal synchronisation in the brain, Physica D , 226 (2), 2007, 181--196. doi:10.1016/j.physd.2006.12.004 F. K. Skinner, H. Bazzazi and S. A. Campbell, Two-cell to N-cell heterogeneous, inhibitory networks: Precise linking of multistable and coherent properties, J. Comput. Neurosci. , 18 (3), 2005, 343--352. doi:10.1007/s10827-005-0331-1 W. W. Lytton, Computer modelling of epilepsy, Nat. Rev. Neurosci. , 9 (8), 2008, 626--637. doi:10.1038/nrn2416 R. D. Traub, A. Bibbig, F. E. N. LeBeau, E. H. Buhl and M. A. Whittington, Cellular mechanisms of neuronal population oscillations in the hippocampus in vitro, Ann. Rev. , 2004. R. D. Traub, A. Draguhn, M. A. Whittington, T. Baldeweg, A. Bibbig, E. H. Buhl and D. Schmitz, Axonal gap junc ions between principal neurons: A novel source of network oscillations, and perhaps epileptogenesis., Rev. Neuroscience , 13 (1), 2002, 1. doi:10.1146/annurev.neuro.27.070203.144303 M. Scheffer, J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, V. Dakos, H. Held, E. H. van Nes, M. Rietkerk and G. Sugihara, Early-warning signals for critical transitions, Nature , 461 (7260), 2009, 53--59. doi:10.1038/nature08227 K. Murphy, A Brief Introduction to Graphical Models and Bayesian Networks , 2008, http://www.cs.ubc.ca/murphyk/Bayes/bnintro.html . R. C. Bradley, An elementary

MRI of postmortem specimens of endangered species for comparative brain anatomy

Many of the 5,500 threatened species of vertebrates found worldwide are highly protected and generally unavailable for scientific investigation. Here we describe a noninvasive protocol to visualize the structure and size of brain in postmortem specimens. We demonstrate its utility by examining four endangered species of kiwi (Apteryx spp.). Frozen specimens are thawed and imaged using MRI, revealing internal details of brain structure. External brain morphology and an estimate of brain volume can be reliably obtained by creating 3D models. This method has facilitated a comparison of brain structure in the different kiwi species, one of which is on the brink of extinction. This new approach has the potential to extend our knowledge of brain structure to species that have until now been outside the reach of anatomical investigation.

Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis

Systems-level identification and analysis of cellular circuits in the brain will require the development of whole-brain imaging with single-cell resolution. To this end, we performed comprehensive chemical screening to develop a whole-brain clearing and imaging method, termed CUBIC (clear, unobstructed brain imaging cocktails and computational analysis). CUBIC is a simple and efficient method involving the immersion of brain samples in chemical mixtures containing aminoalcohols, which enables rapid whole-brain imaging with single-photon excitation microscopy. CUBIC is applicable to multicolor imaging of fluorescent proteins or immunostained samples in adult brains and is scalable from a primate brain to subcellular structures. We also developed a whole-brain cell-nuclear counterstaining protocol and a computational image analysis pipeline that, together with CUBIC reagents, enable the visualization and quantification of neural activities induced by environmental stimulation. CUBIC enables time-course expression profiling of whole adult brains with single-cell resolution.

The structure and connectivity of semantic memory in the healthy older adult brain

The anterior temporal lobes (ATLs) have been proposed to serve as a "hub" linking amodal or domain general information about the meaning of words, objects, facts and people distributed throughout the brain in semantic memory. The two primary sources of evidence supporting this proposal, viz. structural imaging studies in semantic dementia (SD) patients and functional imaging investigations, are not without problems. Similarly, knowledge about the anatomo-functional connectivity of semantic memory is limited to a handful of intra-operative electrocortical stimulation (IES) investigations in patients. Here, using principal components analyses (PCA) of a battery of conceptual and non-conceptual tests coupled with voxel based morphometry (VBM) and diffusion tensor imaging (DTI) in a sample of healthy older adults aged 55-85. years, we show that amodal semantic memory relies on a predominantly left lateralised network of grey matter regions involving the ATL, posterior temporal and posterior inferior parietal lobes, with prominent involvement of the left inferior fronto-occipital fasciculus (IFOF) and uncinate fasciculus fibre pathways. These results demonstrate relationships between semantic memory, brain structure and connectivity essential for human communication and cognition.

Neural mechanisms underlying perilesional transcranial direct current stimulation in aphasia: A feasibility study

Little is known about the neural mechanisms by which transcranial direct current stimulation (tDCS) impacts on language processing in post-stroke aphasia. This was addressed in a proof-of-principle study that explored the effects of tDCS application in aphasia during simultaneous functional magnetic resonance imaging (fMRI). We employed a single subject, cross-over, sham-tDCS controlled design, and the stimulation was administered to an individualized perilesional stimulation site that was identified by a baseline fMRI scan and a picture naming task. Peak activity during the baseline scan was located in the spared left inferior frontal gyrus and this area was stimulated during a subsequent cross-over phase. tDCS was successfully administered to the target region and anodal- vs. sham-tDCS resulted in selectively increased activity at the stimulation site. Our results thus demonstrate that it is feasible to precisely target an individualized stimulation site in aphasia patients during simultaneous fMRI, which allows assessing the neural mechanisms underlying tDCS application. The functional imaging results of this case report highlight one possible mechanism that may have contributed to beneficial behavioral stimulation effects in previous clinical tDCS trials in aphasia. In the future, this approach will allow identifying distinct patterns of stimulation effects on neural processing in larger cohorts of patients. This may ultimately yield information about the variability of tDCS effects on brain functions in aphasia.