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Table 14-3 Main causes of individual and combined oculomotor palsies Lesions of the third (oculomotor) nerve Nuclear and intramedullary (fascicular) Infarction (midbrain stroke) Demyelination Tumor Trauma Wernicke disease Radicular (subarachnoid space and tentorial edge) Aneurysm (posterior communicating or basilar) Meningitis (infectious, neoplastic, granulomatous) Diabetic infarction Tumor Raised intracranial pressure (horizontal shift and herniation of medial temporal lobe, hydrocephalus) Cavernous sinus and superior orbital ssure Diabetic infarction of nerve Aneurysm of internal carotid artery Carotid-cavernous stula Cavernous thrombosis (septic and bland) Tumor (pituitary, meningioma, nasopharyngeal carcinoma, metastasis) Pituitary apoplexy Sphenoid sinusitis and mucocele Herpes zoster Tolosa-Hunt syndrome Macroglobulinemia-hyperviscosity Orbit Trauma Fungal infection (mucormycosis, etc) Tumor and granuloma Orbital pseudotumor Uncertain localization Migraine Postinfectious cranial mono- and polyneuropathy Lesions of the fourth (trochlear) nerve Nuclear and intramedullary (fascicular) Midbrain hemorrhage and infarction Tumor Arteriovenous malformation Demyelination Radicular (subarachnoid space) Traumatic Tumor (pineal, meningioma, metastasis, etc) Hydrocephalus Pseudotumor cerebri and increased intracranial pressure Meningitis (infectious, neoplastic, granulomatous) Raised intracranial pressure

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Cavernous sinus and superior orbital ssure Tumor Tolosa-Hunt syndrome Internal carotid aneurysm Herpes zoster Diabetic infarction Orbit Trauma Tumor and granuloma Lesions of the sixth (abducens) nerve Nuclear (characterized by gaze palsy) and intramedullary (fascicular) Mobius syndrome Wernicke syndrome Infarction (pontine stroke) Demyelination Tumor Lupus Radicular (subarachnoid) Aneurysm Trauma Meningitis Tumor (clivus, fth- and eighth-nerve schwannoma, meningioma) Petrous Infection of mastoid and petrous bone Thrombosis of inferior petrosal vein Trauma Cavernous sinus and superior orbital ssure Carotid aneurysm Cavernous sinus thrombosis Tumor (pituitary, nasopharyngeal, meningioma) Tolosa-Hunt syndrome Diabetic or arteritic infarction Herpes zoster Orbit Tumor and granulomas Uncertain localization Migraine Viral and postviral Transient in newborns

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muscle weakness may be so slight, however, that no strabismus or defect in ocular movement is obvious, yet the patient experiences diplopia It is then necessary to utilize the patient s report of the relative positions of the images of the two eyes as a means of determining which muscle might be involved Several tests are useful for this purpose Two rules are applied sequentially to identify the affected ocular muscle in the analysis of diplopia:

The direction in which the images are maximally separated indicates the direction of action of a pair of muscles at fault For example, if the greatest horizontal separation is in looking to the right, either the right abductor (lateral rectus) or the left adductor (medial rectus) muscle is weak; when looking to the left, either the left lateral rectus and right medial rectus are implicated (Fig 14-6A and B) Restated, if the separation is mainly horizontal, the paresis will be found in one

2 3 4 6 7 5 4 5 4 5 5 4

Acne scars (only minimal effect), ne lines and rhytides, dyschromia (solar lentigines, melasma, postin ammatory hyperpigmentation, ephelides), epidermal and super cial dermal growths (actinic keratoses, seborrheic keratoses, sebaceous hyperplasia, syringomas, trichodiscomas, xanthelasma) Blends areas treated with resurfacing laser Moderate and deep rhytides, actinic keratoses, acne scars, dyschromia (solar lentigines, melasma, postin ammatory hyperpigmentation)

of the horizontally acting recti (a small vertical disparity should be disregarded); if the separation is mainly vertical, the paresis will be found in the vertically acting muscles, and a small horizontal deviation should be disregarded In the position of maximal separation, the image projected farther from the center is attributable to the paretic muscle of the implicated pair that is identi ed in the rst step above The patient may be aided in identifying which image is diverted most by having her several times momentarily uncover one eye and observe which image is most peripheral

Figure 14-6 Diplopia elds with individual muscle paralysis The dark glass is in front of the right eye, and the elds are projected as the patient sees the images A Paralysis of right lateral rectus Characteristic: right eye does not move to the right Field: horizontal homonymous diplopia increasing on looking to the right B Paralysis of right medial rectus Characteristic: right eye does not move to the left Field: horizontal crossed diplopia increasing on looking to the left C Paralysis of right inferior rectus Characteristic: right eye does not move downward when eyes are turned to the right Field: vertical diplopia (image of right eye lowermost) increasing on looking to the right and down D Paralysis of right superior rectus Characteristic: right eye does not move upward when eyes are turned to the right Field: vertical diplopia (image of right eye uppermost) increasing on looking to the right and up E Paralysis of right superior oblique Characteristic: right eye does not move downward when eyes are turned to the left Field: vertical diplopia (image of right eye lowermost) increasing on looking to left and down F Paralysis of right inferior oblique Characteristic: right eye does not move upward when eyes are turned to the left Field: vertical diplopia (image of right eye uppermost) increasing on looking to left and up (Adapted by permission from Cogan DG: Neurology of the Ocular Muscles, 2nd ed Spring eld, IL, Charles C Thomas, 1956)

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Figure 59 Top: Prim s minimum spanning tree algorithm Below: An illustration of Prim s algorithm, starting at node A Also shown are a table of cost/prev values, and the nal MST procedure prim(G, w) Input: A connected undirected graph G = (V, E) with edge weights w e Output: A minimum spanning tree defined by the array prev for all u V : cost(u) = prev(u) = nil Pick any initial node u0 cost(u0 ) = 0 H = makequeue (V ) (priority queue, using cost-values as keys) while H is not empty: v = deletemin(H) for each {v, z} E: if cost(z) > w(v, z): cost(z) = w(v, z) prev(z) = v decreasekey(H, z)

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