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In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.
Methods
To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.
Results
Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.
Conclusions
This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.
An important operation in signal processing and machine learning is dimensionality reduction. There are many such methods, but the starting point is usually linear methods that map data to a lower-dimensional set called a subspace. When working with matrices, the notion of dimension is quantified by rank. This chapter reviews subspaces, span, dimension, rank, and nullspace. These linear algebra concepts are crucial to thoroughly understanding the SVD, a primary tool for the rest of the book (and beyond). The chapter concludes with a machine learning application, signal classification by nearest subspace, that builds on all the concepts of the chapter.
We introduce three measures of complexity for families of sets. Each of the three measures, which we call dimensions, is defined in terms of the minimal number of convex subfamilies that are needed for covering the given family. For upper dimension, the subfamilies are required to contain a unique maximal set, for dual upper dimension a unique minimal set, and for cylindrical dimension both a unique maximal and a unique minimal set. In addition to considering dimensions of particular families of sets, we study the behavior of dimensions under operators that map families of sets to new families of sets. We identify natural sufficient criteria for such operators to preserve the growth class of the dimensions. We apply the theory of our dimensions for proving new hierarchy results for logics with team semantics. To this end we associate each atom with a natural notion or arity. First, we show that the standard logical operators preserve the growth classes of the families arising from the semantics of formulas in such logics. Second, we show that the upper dimension of $k+1$-ary dependence, inclusion, independence, anonymity, and exclusion atoms is in a strictly higher growth class than that of any k-ary atoms, whence the $k+1$-ary atoms are not definable in terms of any atoms of smaller arity.
Let $f: M\rightarrow M$ be a $C^{1+\alpha }$ diffeomorphism on an $m_0$-dimensional compact smooth Riemannian manifold M and $\mu $ a hyperbolic ergodic f-invariant probability measure. This paper obtains an upper bound for the stable (unstable) pointwise dimension of $\mu $, which is given by the unique solution of an equation involving the sub-additive measure-theoretic pressure. If $\mu $ is a Sinai–Ruelle–Bowen (SRB) measure, then the Kaplan–Yorke conjecture is true under some additional conditions and the Lyapunov dimension of $\mu $ can be approximated gradually by the Hausdorff dimension of a sequence of hyperbolic sets $\{\Lambda _n\}_{n\geq 1}$. The limit behaviour of the Carathéodory singular dimension of $\Lambda _n$ on the unstable manifold with respect to the super-additive singular valued potential is also studied.
The ongoing developments of psychiatric classification systems have largely improved reliability of diagnosis, including that of schizophrenia. However, with an unknown pathophysiology and lacking biomarkers, its validity still remains low, requiring further advancements. Research has helped establish multiple sclerosis (MS) as the central nervous system (CNS) disorder with an established pathophysiology, defined biomarkers and therefore good validity and significantly improved treatment options. Before proposing next steps in research that aim to improve the diagnostic process of schizophrenia, it is imperative to recognize its clinical heterogeneity. Indeed, individuals with schizophrenia show high interindividual variability in terms of symptomatic manifestation, response to treatment, course of illness and functional outcomes. There is also a multiplicity of risk factors that contribute to the development of schizophrenia. Moreover, accumulating evidence indicates that several dimensions of psychopathology and risk factors cross current diagnostic categorizations. Schizophrenia shares a number of similarities with MS, which is a demyelinating disease of the CNS. These similarities appear in the context of age of onset, geographical distribution, involvement of immune-inflammatory processes, neurocognitive impairment and various trajectories of illness course. This article provides a critical appraisal of diagnostic process in schizophrenia, taking into consideration advancements that have been made in the diagnosis and management of MS. Based on the comparison between the two disorders, key directions for studies that aim to improve diagnostic process in schizophrenia are formulated. All of them converge on the necessity to deconstruct the psychosis spectrum and adopt dimensional approaches with deep phenotyping to refine current diagnostic boundaries.
Chapter 2: Linearly independent lists of vectors that span a vector space are of special importance. They provide a bridge between the abstract world of vector spaces and the concrete world of matrices. They permit us to define the dimension of a vector space and motivate the concept of matrix similarity.
For a non-conformal repeller $\Lambda $ of a $C^{1+\alpha }$ map f preserving an ergodic measure $\mu $ of positive entropy, this paper shows that the Lyapunov dimension of $\mu $ can be approximated gradually by the Carathéodory singular dimension of a sequence of horseshoes. For a $C^{1+\alpha }$ diffeomorphism f preserving a hyperbolic ergodic measure $\mu $ of positive entropy, if $(f, \mu )$ has only two Lyapunov exponents $\unicode{x3bb} _u(\mu )>0>\unicode{x3bb} _s(\mu )$, then the Hausdorff or lower box or upper box dimension of $\mu $ can be approximated by the corresponding dimension of the horseshoes $\{\Lambda _n\}$. The same statement holds true if f is a $C^1$ diffeomorphism with a dominated Oseledet’s splitting with respect to $\mu $.
Viewing an algebraic number field as a vector space relative to a subfield, which was foreshadowed in Chapter 4, involves varying the field of "scalars" in the definition of vector space. This leads in turn to relative concepts of "basis" and "dimension" which must be taken into account in algebraic number theory. In this chapter we review linear algebra from the ground up, with an emphasis on the relative point of view. This brings some nonstandard results into the picture, such as the Dedekind product theorem and the representation of algebraic numbers by matrices.
This study aimed to analyse the computed tomography parameters for effective ventilation in patients with adhesive otitis media.
Methods
Twenty-six patients with unilateral adhesive otitis media were included in the study. The patients’ temporal bone computed tomography images were retrospectively reviewed. Eustachian tube length and diameter were measured. Mastoid pneumatisation and middle-ear size were evaluated by measuring petroclival and Eustachian tube–tympanic cavity ventilation angles.
Results
The average Eustachian tube length was 38.4 mm and 38.9 mm in adhesive otitis media and healthy ears, respectively. The Eustachian tube diameter of the adhesive otitis media ears (1.47 mm) was significantly narrower than that of the healthy ears (1.83 mm). There were no significant differences in the angles between adhesive otitis media and healthy ears.
Conclusion
A narrow Eustachian tube diameter was associated with developing adhesive otitis media. Measuring Eustachian tube diameter is simple and can be routinely performed when examining temporal bone computed tomography images for Eustachian tube function evaluation.
There are many difficulties in presenting data to the best advantage, using Figures, Tables, images, schemas, etc. Issues such as size, amount of data being included, optimal layout for ease of reading, and other problems are covered. Units, scale bars, statistical indicators, significance and a host of other matters are discussed. The place to present data is considered, i.e. whether some of the slightly less relevant findings or methods should go into 'Supplementary Information'.
Traditional categorical approaches to classifying personality disorders are limited in important ways, leading to a shift in the field to dimensional approaches to conceptualizing personality pathology. Different areas of psychology – personality, developmental, and psychopathology – can be leveraged to understand personality pathology by examining its structure, development, and underlying mechanisms. However, an integrative model that encompasses these distinct lines of inquiry has not yet been proposed. In order to address this gap, we review the latest evidence for dimensional classification of personality disorders based on structural models of maladaptive personality traits, provide an overview of developmental theories of pathological personality, and summarize the Research Domain Criteria (RDoC) initiative, which seeks to understand underlying mechanisms of psychopathology. We conclude by proposing an integrative model of personality pathology development that aims to elucidate the developmental pathways of personality pathology and its underlying mechanisms.
In Aristotle’s Physics we find for the first time motion and speed implicitly measured in terms of time and distance covered, as the discussion of book VI, chapter 2 shows. Aristotle’s explicit account of measurement, however, which he gives in Metaphysics Iota and with which this chapter starts, understands measure not only as homogeneous with the measurand, but also as one-dimensional only. Accordingly, the explicit measure of motion is simply time in the Physics, as we see from examining Aristotle’s understanding of time as the measure and the number of motion. For a full account of motion and speed and a complete response to Zeno’s challenge, however, a complex measure is needed, one that takes account of both time taken and distance covered. The chapter shows that this is exactly what Aristotle implicitly develops in his Physics, when he compares motions of different speed and responds to Zeno’s paradoxes of motion. But it is not what he can accommodate in his theory of measurement.
We shall define a general notion of dimension, and study groups and rings whose interpretable sets carry such a dimension. In particular, we deduce chain conditions for groups, definability results for fields and domains, and show that a pseudofinite
$\widetilde {\mathfrak M}_c$
-group of finite positive dimension contains a finite-by-abelian subgroup of positive dimension, and a pseudofinite group of dimension 2 contains a soluble subgroup of dimension 2.
Taxometric procedures have been used extensively to investigate whether individual differences in personality and psychopathology are latently dimensional or categorical (‘taxonic’). We report the first meta-analysis of taxometric research, examining 317 findings drawn from 183 articles that employed an index of the comparative fit of observed data to dimensional and taxonic data simulations. Findings supporting dimensional models outnumbered those supporting taxonic models five to one. There were systematic differences among 17 construct domains in support for the two models, but psychopathology was no more likely to generate taxonic findings than normal variation (i.e. individual differences in personality, response styles, gender, and sexuality). No content domain showed aggregate support for the taxonic model. Six variables – alcohol use disorder, intermittent explosive disorder, problem gambling, autism, suicide risk, and pedophilia – emerged as the most plausible taxon candidates based on a preponderance of independently replicated findings. We also compared the 317 meta-analyzed findings to 185 additional taxometric findings from 96 articles that did not employ the comparative fit index. Studies that used the index were 4.88 times more likely to generate dimensional findings than those that did not after controlling for construct domain, implying that many taxonic findings obtained before the popularization of simulation-based techniques are spurious. The meta-analytic findings support the conclusion that the great majority of psychological differences between people are latently continuous, and that psychopathology is no exception.
Increasing evidence suggests psychosis may be more meaningfully viewed in dimensional terms rather than as discrete categorical states and that specific symptom clusters may be identified. If so, particular risk factors and premorbid factors may predict these symptom clusters.
Aims.
(i) To explore, using principal component analysis, whether specific factors for psychotic symptoms can be isolated. (ii) To establish the predictors of the different symptom factors using multiple regression techniques.
Method.
One hundred and eighty-nine inpatients with psychotic illness were recruited and information on family history, premorbid factors and current symptoms obtained from them and their mothers.
Results.
Seven distinct symptom components were identified. Regression analysis failed to identify any developmental predictors of depression or mania. Delusions/hallucinations were predicted by a family history of schizophrenia and by poor school functioning in spite of normal premorbid IQ (F = 6.5; P < 0.001); negative symptoms by early onset of illness, developmental delay and a family history of psychosis (F = 4.1; P = 0.04). Interestingly disorganisation was predicted by the combination of family history of bipolar disorder and low premorbid IQ (F = 4.9; P = 0.003), and paranoia by obstetric complications (OCs) and poor school functioning (F = 4.2; P = 0.01).
Conclusion.
Delusions and hallucinations, negative symptoms and paranoia all appeared to have a developmental origin though they were associated with different childhood problems. On the other hand, neither mania nor depression was associated with childhood dysfunction. Our most striking finding was that disorganisation appeared to arise when a familial predisposition to mania was compounded by low premorbid IQ.
To examine the predictive diagnostic value of affective symptomatology in a first-episode psychosis (FEP) sample with 5 years’ follow-up.
Method
Affective dimensions (depressive, manic, activation, dysphoric) were measured at baseline and 5 years in 112 FEP patients based on a factor structure analysis using the Young Mania Rating Scale and Hamilton Depression Rating Scale. Patients were classified as having a diagnosis of bipolar disorder at baseline (BDi), bipolar disorder at 5 years (BDf), or “other psychosis”. The ability of affective dimensions to discriminate between these diagnostic groups and to predict a bipolar disorder diagnosis was analysed.
Results
Manic dimension score was higher in BDi vs. BDf, and both groups had higher manic and activation scores vs. “other psychosis”. Activation dimension predicted a bipolar diagnosis at 5 years (odds ratio = 1.383; 95% confidence interval, 1.205–1.587; P = 0.000), and showed high levels of sensitivity (86.2%), specificity (71.7%), positive (57.8%) and negative predictive value (90.5%). Absence of the manic dimension and presence of the depressive dimension were both significant predictors of an early misdiagnosis.
Conclusion
The activation dimension is a diagnostic predictor for bipolar disorder in FEP. The manic dimension contributes to a bipolar diagnosis and its absence can lead to early misdiagnosis.
Regular polytopes and their symmetry have a long history stretching back two and a half millennia, to the classical regular polygons and polyhedra. Much of modern research focuses on abstract regular polytopes, but significant recent developments have been made on the geometric side, including the exploration of new topics such as realizations and rigidity, which offer a different way of understanding the geometric and combinatorial symmetry of polytopes. This is the first comprehensive account of the modern geometric theory, and includes a wide range of applications, along with new techniques. While the author explores the subject in depth, his elementary approach to traditional areas such as finite reflexion groups makes this book suitable for beginning graduate students as well as more experienced researchers.
This paper addresses some questions about dimension theory for P-minimal structures. We show that, for any definable set A, the dimension of $\bar A\backslash A$ is strictly smaller than the dimension of A itself, and that A has a decomposition into definable, pure-dimensional components. This is then used to show that the intersection of finitely many definable dense subsets of A is still dense in A. As an application, we obtain that any definable function $f:D \subseteq {K^m} \to {K^n}$ is continuous on a dense, relatively open subset of its domain D, thereby answering a question that was originally posed by Haskell and Macpherson.
In order to obtain these results, we show that P-minimal structures admit a type of cell decomposition, using a topological notion of cells inspired by real algebraic geometry.